How to Run 10+ Persistent Agents with OpenClaw
Running one AI agent is easy. Running ten or more simultaneously without them breaking, conflicting, or burning your budget requires a different architecture. Here's the exact setup I'd use today.
Most people who try running multiple persistent agents hit the same wall: they get one or two working, feel great about it, then try to spin up five more and everything falls apart. Agents start talking over each other. State gets corrupted. Your API bill looks like a phone number. Or worst of all, the whole thing just silently breaks and you don't realize it until a user complains.
I've been there. And after spending way too many hours debugging multi-agent setups, I can tell you the problem usually isn't the concept ā it's the infrastructure. Running 10+ persistent agents simultaneously is entirely doable with OpenClaw, but you need to think about it differently than running a single agent. It's less like "copy-paste the agent config ten times" and more like "architect a small distributed system."
Let me walk you through exactly how I'd set it up today, from scratch, if I needed 10+ agents running persistently and reliably.
Why "Persistent" Changes Everything
First, let's be clear about what we mean by persistent agents. A persistent agent isn't just a script that runs, does a thing, and exits. It's an agent that:
- Stays alive across sessions, maintaining state and memory
- Listens for incoming messages or events continuously
- Remembers what happened in previous interactions
- Recovers from failures without losing context
This is fundamentally different from firing off a one-shot agent call. Persistent agents need managed state, resource allocation, error recovery, and some kind of orchestration layer to keep everything humming. OpenClaw was built with this in mind, which is why it's the right tool for this job.
The Architecture: Think Teams, Not Individual Agents
The biggest mistake people make is thinking about each agent in isolation. When you're running 10+ agents, you need to think in terms of teams and communication patterns. Here's the mental model that works:
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
ā Orchestrator ā
ā (routes, monitors, controls) ā
āāāāāāāāāāāā¬āāāāāāāāāāā¬āāāāāāāāāāāāāāāā¤
ā Team A ā Team B ā Team C ā
ā Agent 1 ā Agent 4 ā Agent 8 ā
ā Agent 2 ā Agent 5 ā Agent 9 ā
ā Agent 3 ā Agent 6 ā Agent 10 ā
ā ā Agent 7 ā Agent 11 ā
āāāāāāāāāāāā“āāāāāāāāāāā“āāāāāāāāāāāāāāāā
Group agents by function. Give each group a shared context. Let the orchestrator handle routing between groups. This is how you avoid the "agents talking past each other" problem that kills most multi-agent setups.
Step 1: Define Your Agents with Resource Boundaries
The first thing you need is a clear definition for each agent that includes not just what it does, but what it's allowed to consume. This is where most tutorials fail you ā they show you how to create an agent but not how to constrain it.
from openclaw import Agent, CostLimits, StateStore, LLMProvider
# Define agents with explicit resource boundaries
agents_config = [
{
"name": "intake_router",
"role": "Route incoming requests to the right specialist agent",
"llm": LLMProvider.openai("gpt-4o-mini"), # Cheap model for routing
"cost_limits": CostLimits(
max_per_task=0.05,
max_per_session=2.00,
max_iterations=5
),
"priority": "high" # Always responsive
},
{
"name": "deep_researcher",
"role": "Conduct thorough research on complex topics",
"llm": LLMProvider.anthropic("claude-3-opus"), # Heavy model for deep work
"cost_limits": CostLimits(
max_per_task=1.00,
max_per_session=15.00,
max_iterations=20
),
"priority": "normal"
},
{
"name": "code_analyst",
"role": "Review and analyze code submissions",
"llm": LLMProvider.openai("gpt-4"),
"cost_limits": CostLimits(
max_per_task=0.50,
max_per_session=8.00,
max_iterations=15
),
"priority": "normal"
},
# ... define all 10+ agents similarly
]
Notice the key decisions here:
Different models for different jobs. Your routing agent doesn't need GPT-4. Use a cheaper, faster model for simple decisions and save the heavy hitters for agents that need real reasoning power. OpenClaw lets you mix providers freely ā Claude for one agent, GPT-4 for another, a local Ollama model for a third. No lock-in.
Explicit cost limits on every agent. This is non-negotiable when running 10+ agents. Without limits, one runaway agent can burn through your entire budget in minutes. The max_iterations parameter is your circuit breaker against infinite loops.
Priority levels. When you're running many agents, some need to be more responsive than others. Your router should always be fast. Your deep researcher can take its time.
Step 2: Set Up Persistent State
Persistent agents need persistent memory. Full stop. If your agents forget everything when they restart, they're not really persistent ā they're just long-running.
from openclaw import Agent, StateStore
# Use a real database for production persistence
state_backend = StateStore.postgres("postgresql://user:pass@localhost:5432/openclaw_agents")
# For development, file-based is fine
# state_backend = StateStore.file("./agent_states")
# Each agent gets its own state namespace
researcher = Agent(
name="deep_researcher",
state_store=state_backend,
llm=LLMProvider.anthropic("claude-3-opus"),
cost_limits=CostLimits(max_per_session=15.00, max_iterations=20)
)
@researcher.on_message
async def handle_research(message, state):
# Retrieve accumulated knowledge from previous sessions
knowledge_base = state.memory.get("accumulated_findings", [])
active_projects = state.memory.get("active_projects", {})
# Do the research
new_findings = await conduct_research(message.content, context=knowledge_base)
# State automatically persists ā survives restarts, crashes, deployments
knowledge_base.append({
"query": message.content,
"findings": new_findings,
"timestamp": datetime.now().isoformat()
})
state.memory["accumulated_findings"] = knowledge_base
return new_findings
The beauty of OpenClaw's state management is that you don't need to write serialization logic, manage database connections, or worry about race conditions. You just read and write to state.memory and the framework handles the rest. When you call agent.resume_session("session_id"), everything comes back exactly as it was.
For 10+ agents, I recommend Postgres or Redis as your state backend. File-based storage works for development but will cause issues at scale when multiple agents try to write simultaneously.
Step 3: Wire Up Communication with Capability-Based Routing
This is where the magic happens and where most people's multi-agent setups fall apart. When you have 10+ agents, you can't manually route every message. You need the system to figure out who should handle what.
from openclaw import AgentTeam, Message, Pipeline, ParallelGroup
team = AgentTeam(state_store=state_backend)
# Register agents with explicit capabilities
@team.agent(name="billing_specialist", handles=["payment", "invoice", "refund", "subscription", "pricing"])
async def billing_agent(message: Message, state):
history = state.memory.get("billing_interactions", [])
result = await process_billing_request(message, history=history)
history.append({"query": message.content, "result": result})
state.memory["billing_interactions"] = history
return result
@team.agent(name="technical_support", handles=["bug", "error", "integration", "api", "sdk", "deployment"])
async def tech_agent(message: Message, state):
known_issues = state.memory.get("known_issues", [])
result = await troubleshoot(message, known_issues=known_issues)
return result
@team.agent(name="account_manager", handles=["account", "profile", "settings", "permissions", "access"])
async def account_agent(message: Message, state):
return await manage_account(message)
@team.agent(name="content_writer", handles=["blog", "documentation", "copy", "email", "announcement"])
async def content_agent(message: Message, state):
style_guide = state.memory.get("style_preferences", {})
return await write_content(message, style=style_guide)
@team.agent(name="data_analyst", handles=["metrics", "analytics", "report", "dashboard", "trends"])
async def analytics_agent(message: Message, state):
return await analyze_data(message)
@team.agent(name="qa_reviewer", handles=["review", "quality", "check", "verify", "test"])
async def qa_agent(message: Message, state):
return await review_output(message)
# Six agents registered. The team auto-routes based on message content.
# "What's my invoice total?" ā billing_specialist
# "The API is returning 500 errors" ā technical_support
# "Write a blog post about our new feature" ā content_writer
OpenClaw's capability-based routing uses the handles keywords to determine which agent should receive a message, but it's smarter than simple keyword matching. It maintains a conversation graph that tracks context, so if someone starts with a billing question and then says "actually, can you also check my account settings?" the framework knows to hand off to the account manager while preserving the conversation context.
Step 4: Enable Parallelization for Performance
With 10+ agents, sequential execution will kill your performance. If a user triggers a workflow that needs research, analysis, and content generation, those independent tasks should run simultaneously.
from openclaw import Pipeline, ParallelGroup, Agent
# Complex workflow with parallel execution
analysis_pipeline = Pipeline([
# Step 1: Single agent gathers requirements
Agent("intake_router").does("Parse the request and identify needed analyses"),
# Step 2: Multiple agents work simultaneously
ParallelGroup([
Agent("data_analyst").does("Pull relevant metrics and trends"),
Agent("deep_researcher").does("Research competitive landscape"),
Agent("content_writer").does("Draft initial framework for the report"),
]),
# Step 3: Synthesize (waits for all parallel agents to finish)
Agent("report_synthesizer").does("Combine all inputs into a coherent report"),
# Step 4: Quality check
Agent("qa_reviewer").does("Review the final report for accuracy and completeness")
])
# This runs in ~60 seconds instead of ~4 minutes sequential
result = await analysis_pipeline.run("Q3 competitive analysis for enterprise segment")
# Visualize what happened
analysis_pipeline.visualize()
The ParallelGroup is the single biggest performance win you'll get. In my experience, proper parallelization cuts total workflow time by 60-70% for most multi-agent tasks. And because each agent within the parallel group has its own cost limits and state, a failure in one doesn't bring down the others.
Step 5: Make It Production-Ready
Here's where the difference between "cool demo" and "reliable system" shows up. Running 10+ persistent agents in production means you need retry logic, observability, and graceful degradation.
from openclaw import Agent, RetryPolicy, Observability, HealthCheck
# Production configuration for each agent
def create_production_agent(name, llm, cost_limits, handles):
return Agent(
name=name,
llm=llm,
cost_limits=cost_limits,
state_store=state_backend,
retry_policy=RetryPolicy(
max_attempts=3,
backoff="exponential",
retry_on=[RateLimitError, TimeoutError, ConnectionError]
),
observability=Observability(
metrics_endpoint=os.getenv("METRICS_URL"),
alert_on_failure=True,
trace_sampling=0.1 # Sample 10% of requests for detailed tracing
),
health_check=HealthCheck(
interval=30, # Check every 30 seconds
timeout=10,
on_unhealthy="restart" # Auto-restart unhealthy agents
)
)
# Create all production agents
agents = []
for config in agents_config:
agent = create_production_agent(**config)
agents.append(agent)
The HealthCheck configuration is critical for persistent agents. Agents can become unresponsive for many reasons ā memory leaks, stuck API calls, corrupted state. The health check monitors each agent and automatically restarts unhealthy ones, rehydrating their state from the persistent store. Your 10+ agents stay alive without you babysitting them.
The observability layer gives you Prometheus-compatible metrics out of the box:
openclaw_agent_requests_total{agent="billing_specialist", status="success"} 3847
openclaw_agent_requests_total{agent="billing_specialist", status="failure"} 12
openclaw_agent_latency_seconds{agent="deep_researcher", quantile="0.95"} 4.21
openclaw_agent_cost_dollars{agent="deep_researcher"} 67.34
openclaw_active_agents_count 11
openclaw_state_store_operations_total{operation="write"} 28453
Pipe these into Grafana or Datadog and you'll know exactly what your agent fleet is doing at all times.
Step 6: Debug Without Losing Your Mind
When something goes wrong with one of your 10+ agents ā and it will ā you need to be able to pinpoint the issue fast. OpenClaw's debug mode and session replay are lifesavers here.
from openclaw import Agent, DebugMode
# Enable verbose debugging for a specific agent
problem_agent = Agent(
name="data_analyst",
debug=DebugMode.VERBOSE
)
# This produces execution traces like:
"""
[10:23:45.123] AGENT:data_analyst | ACTION:receive_message
āā message_id: msg_8f2a
āā source: intake_router
āā context_tokens: 2,341
āā routing_confidence: 0.94
[10:23:47.456] AGENT:data_analyst | ACTION:state_read
āā keys_accessed: ['cached_metrics', 'query_history']
āā state_size: 14.2 KB
[10:23:48.789] AGENT:data_analyst | ACTION:llm_call
āā model: gpt-4
āā prompt_tokens: 3,456
āā completion_tokens: 892
āā latency: 2.34s
āā cost: $0.052
[10:23:51.012] AGENT:data_analyst | ACTION:send_response
āā target: report_synthesizer
āā response_tokens: 892
āā confidence: 0.88
"""
# Replay a specific session to reproduce bugs
problem_agent.replay_session("session_xyz789")
The session replay feature is particularly valuable for multi-agent debugging. When agents interact in complex ways, being able to replay the exact sequence of messages, decisions, and state changes makes it possible to identify issues that would otherwise take hours of log-spelunking.
Step 7: Test Without Going Broke
Running tests against 10+ agents using real API calls is financially insane. OpenClaw's testing utilities let you validate your entire multi-agent system for free.
from openclaw import Agent, MockLLM
from openclaw.testing import AgentTestCase, TeamTestCase
class TestAgentTeam(TeamTestCase):
def setUp(self):
# Mock all LLM calls with deterministic responses
self.team = AgentTeam(
llm_override=MockLLM(responses={
"billing": "Processing your billing inquiry...",
"technical": "Let me diagnose that technical issue...",
"routing": "Directing to billing_specialist..."
})
)
async def test_routing_accuracy(self):
"""Verify messages route to the correct agent"""
response = await self.team.process("I need a refund on my last invoice")
self.assertEqual(response.handled_by, "billing_specialist")
async def test_parallel_execution(self):
"""Verify parallel groups complete correctly"""
pipeline = self.create_test_pipeline()
result = await pipeline.run("Generate quarterly report")
self.assertEqual(len(result.parallel_results), 3)
self.assertTrue(all(r.success for r in result.parallel_results))
async def test_state_persistence(self):
"""Verify state survives across interactions"""
await self.team.process("Remember: project deadline is March 15")
await self.team.process("When is the project deadline?")
self.assertIn("March 15", self.team.last_response.content)
async def test_cost_limits_enforced(self):
"""Verify agents respect cost boundaries"""
with self.assertRaises(CostLimitExceeded):
for _ in range(100):
await self.team.agents["deep_researcher"].process("expensive query")
# Run the full suite ā zero API calls, zero cost
# python -m pytest tests/test_agents.py -v
You can also record real interactions and replay them in tests:
# Record once (costs money, but only once)
with team.record_mode(save_to="fixtures/agent_interactions.json"):
await team.process("Complex real-world query")
# Replay forever (free, deterministic)
team.load_fixtures("fixtures/agent_interactions.json")
await team.process("Complex real-world query") # Uses recorded response
The Quick Start Shortcut
If reading all of this made you think "this is a lot of setup," you're not wrong. Configuring 10+ agents with proper state management, cost controls, routing, health checks, and testing infrastructure is real engineering work.
If you don't want to set all of this up manually, Felix's OpenClaw Starter Pack on Claw Mart is genuinely the fastest way I've found to get going. For $29, you get pre-configured agent skills, ready-made team templates, and the boilerplate orchestration code already written. It covers the exact patterns I described above ā persistent state, capability-based routing, cost controls, the works. I spent a weekend building what Felix's pack gives you out of the box, and frankly his configurations are better than what I came up with on my first try. It's not a magic bullet, but it eliminates the tedious scaffolding so you can focus on what your agents actually do rather than how they communicate and persist.
Resource Allocation Rules of Thumb
After running multi-agent setups for a while, here are the rough guidelines I follow:
Budget allocation across agents: Your routing/orchestrator agents should consume less than 5% of your total budget. They're doing lightweight work. Allocate 60-70% to your "worker" agents that do the heavy lifting, and keep 25-30% as buffer for retries and unexpected spikes.
Model selection: Use the cheapest model that gets the job done for each agent. Not every agent needs GPT-4 or Claude Opus. Routing agents, formatters, and simple classifiers work fine with GPT-4o-mini or even smaller models. Save the expensive models for agents doing genuine reasoning, analysis, or creative work.
State storage: For 10+ agents, use Postgres. Redis is great for speed but less reliable for long-term persistence. File-based storage breaks when multiple agents write simultaneously. Postgres handles concurrent writes, gives you backups, and scales well beyond 10 agents.
Monitoring priority: Set up alerts for three things first: cost spikes (any agent exceeding 2x its normal spend), failure rates (any agent failing more than 5% of requests), and latency (any agent taking more than 3x its P50 response time). Everything else is nice-to-have.
Common Pitfalls to Avoid
Don't give every agent full conversation history. When agents share too much context, they get confused and responses get slower (more tokens = more cost and latency). Use OpenClaw's conversation graph to give each agent only the relevant context.
Don't skip the circuit breakers. I know max_iterations=10 feels limiting, but one agent stuck in a loop will drain your budget faster than you can check your email. You can always increase limits later for specific tasks.
Don't run all agents at the same priority. If everything is high priority, nothing is. Your router needs to be fast. Your deep researcher can take 30 seconds. Set priorities accordingly and let OpenClaw allocate resources appropriately.
Don't forget to test the failure modes. Your happy path will work fine. What happens when one agent in a parallel group fails? What happens when the state store is temporarily unavailable? What happens when an agent hits its cost limit mid-task? Test these scenarios with mocks before they happen in production.
What's Next
Once you have 10+ agents running persistently and reliably, the natural next step is optimization. Use OpenClaw's built-in metrics to identify which agents are over-provisioned (spending budget but not adding value) and which are bottlenecks (high latency, frequent retries). Swap models, adjust cost limits, and tune your routing rules based on real data.
The framework gives you everything you need to iterate quickly. The hard part ā the architecture, the state management, the communication patterns ā is already solved. Now you just need to make your agents actually good at their jobs.
Start with three or four agents, get them solid, then scale to 10+. Or grab Felix's Starter Pack and start with 10 from day one. Either way, OpenClaw makes it possible without the infrastructure headaches that kill most multi-agent projects before they ship.