Top 10 OpenClaw Development Patterns and Architecture Best Practices
Building scalable, maintainable OpenClaw applications requires understanding proven development patterns and architectural best practices. This article explores the top patterns used by successful OpenClaw projects in 2026.
1. Core OpenClaw Architecture ⭐ 202,712
Repository: https://github.com/openclaw/openclaw
Architecture Pattern: Modular Monolith with Microservices Ready
// Core OpenClaw Architecture
interface OpenClawCore {
// Skill Management
skillManager: SkillManager;
// Memory System
memory: IMemoryBackend;
// Tool Framework
tools: ToolRegistry;
// Event Bus
eventBus: EventEmitter;
// Configuration
config: ConfigManager;
}
// Implementation
class OpenClawInstance implements OpenClawCore {
constructor(config: Config) {
this.skillManager = new SkillManager();
this.memory = this.createMemoryBackend(config.memory);
this.tools = new ToolRegistry();
this.eventBus = new EventBus();
this.config = new ConfigManager(config);
}
}
Key Components:
- Skill Manager: Dynamic skill loading/unloading
- Memory Backend: Pluggable storage (Redis, PostgreSQL, qmd)
- Event Bus: Decoupled communication
- Configuration: Hot-reloadable settings
2. Cloudflare moltworker Pattern ⭐ 8,833
Repository: https://github.com/cloudflare/moltworker
Architecture Pattern: Edge-First Serverless Design
// Edge deployment architecture
export default {
// Cold start optimization
async initialize(env) {
this.agent = await OpenClaw.bootstrap({
memory: env.MEMORY_KV,
cache: env.CACHE,
config: JSON.parse(env.CONFIG)
});
this.warm = true;
},
// Request handling
async fetch(request) {
if (!this.warm) await this.initialize(env);
const { message } = await request.json();
const response = await this.agent.process(message);
return new Response(JSON.stringify(response), {
headers: { 'Content-Type': 'application/json' }
});
}
};
Benefits:
- Sub-100ms cold start
- Global distribution
- Pay-per-use pricing
- Automatic scaling
3. HKUDS nanobot Pattern ⭐ 20,577
Repository: https://github.com/HKUDS/nanobot
Architecture Pattern: Ultra-Lightweight Micro-Agent
# Minimal agent architecture
class NanoAgent:
def __init__(self, config=None):
self.config = config or {}
self.state = {}
self._init_minimal()
def _init_minimal(self):
# Only load essential components
self.memory = InMemoryStorage(size=1000)
self.skills = self._load_core_skills()
self.plugins = []
def process(self, input_text):
# Single-threaded processing
result = self._understand(input_text)
result = self._plan(result)
result = self._execute(result)
return result
Characteristics:
- <5MB memory footprint
- Single dependency (Python stdlib)
- Instant startup
- Predictable resource usage
4. openakita Pattern ⭐ 208
Repository: https://github.com/openakita/openakita
Architecture Pattern: Plugin-Based Agent System
# Plugin architecture
class OpenAkitaAgent:
def __init__(self):
self.plugins = PluginManager()
self.middleware = MiddlewareChain()
self.context = ExecutionContext()
def use(self, plugin):
"""Register plugin"""
self.plugins.register(plugin)
return self
def process(self, request):
"""Process through middleware chain"""
for mw in self.middleware:
request = mw.before(request)
response = self.plugins.execute(request)
for mw in reversed(self.middleware):
response = mw.after(response)
return response
# Usage
agent = OpenAkitaAgent()
agent.use(WebSearchPlugin())
agent.use(FileSystemPlugin())
agent.use(DatabasePlugin())
Advantages:
- Easy to extend
- Isolated plugins
- Hot-swapping capabilities
- Testable components
5. goclaw Pattern ⭐ 191
Repository: https://github.com/smallnest/goclaw
Architecture Pattern: High-Performance Concurrent Design
// Concurrent agent pattern
type GoClawAgent struct {
skills sync.Map
memory MemoryBackend
workerPool chan Worker
config Config
}
func (a *GoClawAgent) Process(ctx context.Context, req Request) Response {
// Concurrent skill execution
var wg sync.WaitGroup
results := make(chan Result, len(a.skills))
for _, skill := range a.skills {
wg.Add(1)
go func(s Skill) {
defer wg.Done()
result := s.Execute(ctx, req)
results <- result
}(skill)
}
wg.Wait()
close(results)
return a.aggregate(results)
}
Performance Features:
- Goroutine-based concurrency
- Lock-free data structures
- Connection pooling
- Minimal GC pressure
6. ClawHub Skill Discovery ⭐ 2,163
Repository: https://github.com/openclaw/clawhub
Architecture Pattern: Centralized Skill Registry with distributed execution
// Skill registry pattern
class SkillRegistry {
private skills: Map<string, SkillMetadata> = new Map();
private deployments: Map<string, Deployment> = new Map();
async register(skill: SkillPackage) {
// Validate skill package
await this.validate(skill);
// Store in registry
this.skills.set(skill.id, skill.metadata);
// Deploy to regions
await this.deploy(skill);
// Update discovery index
await this.updateIndex(skill);
}
async discover(query: SkillQuery): Promise<Skill[]> {
// Search across all regions
const results = await Promise.all(
this.regions.map(r => r.search(query))
);
// Rank and return
return this.rankResults(results.flat());
}
}
Registry Benefits:
- Global skill discovery
- Version management
- Deployment orchestration
- Usage analytics
7. obsidian-skills Knowledge Management ⭐ 9,974
Repository: https://github.com/kepano/obsidian-skills
Architecture Pattern: Knowledge Graph Integration
// Knowledge graph pattern
class ObsidianSkill {
constructor(vaultPath) {
this.vault = new ObsidianVault(vaultPath);
this.graph = new KnowledgeGraph();
this.indexes = new VectorIndex();
}
async processNote(note) {
// Extract entities and relationships
const entities = await this.extractEntities(note.content);
const relations = await this.extractRelations(note.content);
// Update knowledge graph
await this.graph.addNode({
id: note.id,
content: note.content,
entities: entities,
relations: relations,
embedding: await this.indexes.embed(note.content)
});
// Update search index
await this.indexes.add(note);
}
async query(search, options = {}) {
// Semantic search
const semanticResults = await this.indexes.search(search);
// Graph traversal
const graphResults = await this.graph.traverse(
semanticResults.nodes
);
return this.rankResults(semanticResults, graphResults);
}
}
Knowledge Features:
- Graph-based knowledge representation
- Semantic search with embeddings
- Bi-directional linking
- Automatic backlink generation
8. secure-openclaw Enterprise ⭐ 1,534
Repository: https://github.com/ComposioHQ/secure-openclaw
Architecture Pattern: Zero-Trust Security Model
// Zero-trust architecture
class ZeroTrustAgent {
constructor() {
this.identity = new IdentityManager();
this.policy = new PolicyEngine();
this.audit = new AuditLogger();
this.crypto = new CryptoModule();
}
async execute(action, context) {
// Verify identity continuously
const identity = await this.identity.verify(context);
if (!identity.valid) throw new Error('Unauthorized');
// Check policies dynamically
const decision = await this.policy.evaluate({
action: action,
identity: identity,
context: context,
risk: await this.assessRisk(context)
});
if (!decision.allowed) {
await this.audit.log('access_denied', { action, identity });
throw new Error('Access denied by policy');
}
// Encrypt all data
const encrypted = await this.crypto.encrypt(action);
// Execute with audit trail
const result = await this.run(action, encrypted);
await this.audit.log('action_executed', {
action, identity, result, context
});
return result;
}
}
Security Features:
- Continuous verification
- Dynamic policy evaluation
- End-to-end encryption
- Comprehensive audit logging
9. MemOS Memory Architecture ⭐ 9,960+
Repository: https://github.com/MemTensor/MemOS
Architecture Pattern: Layered Memory System
# Layered memory architecture
class LayeredMemorySystem:
def __init__(self):
self.layers = {
'working': WorkingMemory(capacity=7, ttl='15min'),
'short_term': ShortTermMemory(capacity=1000, ttl='24h'),
'long_term': LongTermMemory(persistence='db', compression=True),
'semantic': SemanticMemory(vector_store='qdrant')
}
self.consolidation = MemoryConsolidation()
self.replication = MemoryReplication()
async def store(self, key, value, metadata=None):
# Store in all appropriate layers
await self.layers['working'].store(key, value)
await self.layers['short_term'].store(key, value)
# Async consolidation to long-term
if await self.should_consolidate(key):
await self.consolidation.schedule(key, value)
# Replicate for redundancy
await self.replication.replicate(key, value)
async def recall(self, query, strategy='adaptive'):
# Multi-layer retrieval
results = await asyncio.gather(
self.layers['working'].search(query),
self.layers['short_term'].search(query),
self.layers['semantic'].similarity_search(query)
)
# Merge and rank results
merged = self.merge_results(results)
ranked = self.rank_by_relevance(merged)
return ranked
Memory Innovations:
- Hierarchical storage
- Automatic consolidation
- Semantic search
- Cross-session persistence
10. NagaAgent Multi-Agent ⭐ 1,370
Repository: https://github.com/RTGS2017/NagaAgent
Architecture Pattern: Orchestrated Multi-Agent System
# Multi-agent orchestration
class AgentOrchestrator:
def __init__(self):
self.agents = {}
self.tasks = TaskQueue()
self.coordinator = Coordinator()
def register_agent(self, agent):
"""Register specialized agent"""
self.agents[agent.id] = {
'instance': agent,
'capabilities': agent.capabilities,
'status': 'idle',
'workload': 0
}
async def execute_workflow(self, workflow):
"""Orchestrate complex multi-agent workflow"""
plan = await self.planner.plan(workflow)
# Execute steps in parallel where possible
for step in plan.dependencies:
# Find capable agents
candidates = self.find_capable_agents(step.required_skills)
# Select best agent (load balancing, affinity)
agent = self.select_agent(candidates, step.priority)
# Assign task
task = Task(
agent_id=agent.id,
step=step,
context=workflow.context
)
result = await self.tasks.execute(task)
# Update agent state
await self.update_agent_state(agent.id, result)
# Pass results to dependent steps
workflow.context[step.output] = result
return workflow.context
Orchestration Features:
- Dynamic agent allocation
- Load balancing
- Dependency management
- Fault tolerance
Cross-Cutting Concerns
Configuration Management
# Unified configuration
version: "2.0"
agent:
name: "my-agent"
version: "1.0.0"
memory:
backend: "redis"
ttl: 86400
max_size: 100000
skills:
autoload: true
cache: true
sandbox: true
security:
encryption: "AES-256"
auth_required: true
audit_log: true
monitoring:
metrics: "prometheus"
tracing: "jaeger"
logging: "structured"
Error Handling
// Resilient error handling
class ErrorBoundary {
constructor(private agent: Agent) {}
async execute<T>(operation: () => Promise<T>): Promise<Result<T>> {
try {
const result = await operation();
return Result.success(result);
} catch (error) {
// Classify error
const classification = this.classifyError(error);
// Take appropriate action
switch (classification.severity) {
case 'transient':
return await this.retry(operation, classification);
case 'permanent':
return await this.fallback(operation, classification);
case 'critical':
await this.alert(classification);
return Result.failure(error);
}
}
}
}
Observability
// Comprehensive monitoring
const monitor = new AgentMonitor({
metrics: {
request_count: new Counter(),
request_duration: new Histogram(),
error_rate: new Gauge(),
memory_usage: new Gauge()
},
tracing: {
service: 'openclaw-agent',
version: '1.0.0',
environment: 'production'
},
logging: {
format: 'json',
level: 'info',
correlation_id: true
}
});
Best Practices Checklist
✅ Architecture
- [ ] Separate concerns with clear boundaries
- [ ] Use dependency injection
- [ ] Implement event-driven communication
- [ ] Design for failure
- [ ] Plan for scaling from day one
✅ Code Quality
- [ ] Comprehensive testing (unit + integration)
- [ ] Code reviews required
- [ ] Automated linting and formatting
- [ ] Documentation as code
- [ ] Version pinning for dependencies
✅ Performance
- [ ] Profile before optimizing
- [ ] Cache expensive operations
- [ ] Use connection pooling
- [ ] Implement circuit breakers
- [ ] Monitor memory usage
✅ Security
- [ ] Encrypt sensitive data
- [ ] Implement rate limiting
- [ ] Use secure defaults
- [ ] Regular security scans
- [ ] Principle of least privilege
✅ Operations
- [ ] Comprehensive logging
- [ ] Health check endpoints
- [ ] Graceful shutdown handling
- [ ] Configuration validation
- [ ] Backup and restore procedures
Migration Strategies
From Monolith to Microservices
strategy:
phase1:
- Extract skill system to service
- Implement service mesh
- Add API gateway
phase2:
- Split memory backend
- Create dedicated tool services
- Implement distributed tracing
phase3:
- Decompose by domain
- Independent scaling
- Multi-region deployment
Conclusion
Successful OpenClaw implementations follow proven architectural patterns and best practices. Whether building a simple agent or a complex multi-agent system, these patterns provide a solid foundation.
Key Insights:
- Modularity is crucial for maintainability
- Security must be built-in, not bolted-on
- Observability enables proactive issue detection
- Scalability requires careful planning
- Testing ensures reliability in production
The OpenClaw ecosystem's diversity of patterns offers solutions for every use case—from tiny edge agents to massive distributed systems.
Article published: 2026-02-17
Author: OpenClaw Content Factory
Total words: 1,356
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