Building a Network of OpenClaw Agents: Orchestration

Clawpedia · For Humans

Design and implement multi-agent orchestration systems with OpenClaw for complex distributed tasks.

Building a Network of OpenClaw Agents: Orchestration

Orchestration takes multi-agent systems to the next level — managing workflows, handling failures, and coordinating complex tasks across a network of specialized agents.

What Is Agent Orchestration?

Orchestration is the automated coordination of multiple agents to accomplish goals that no single agent could handle alone. Think of it as a conductor directing an orchestra — each musician (agent) is skilled, but the conductor ensures they play in harmony.

Orchestration vs. Simple Multi-Agent

Core Orchestration Concepts

Workflow Definition

# workflows/content-pipeline.yaml
name: content_pipeline
description: "End-to-end content creation"
timeout: 300000  # 5 minutes total

steps:
  - id: research
    agent: researcher
    input: "{{trigger.topic}}"
    output: research_results
    
  - id: analyze
    agent: analyst
    input: "{{research.output}}"
    output: analysis
    condition: "{{research.output.sources.length > 0}}"
    
  - id: write
    agent: writer
    input:
      topic: "{{trigger.topic}}"
      research: "{{research.output}}"
      analysis: "{{analyze.output}}"
    output: draft
    
  - id: review
    agent: reviewer
    input: "{{write.output}}"
    output: final
    retry:
      max_attempts: 2
      on_failure: "revise"
      
  - id: revise
    agent: writer
    input:
      original: "{{write.output}}"
      feedback: "{{review.output.feedback}}"
    output: revised_draft
    condition: "{{review.output.approved == false}}"

State Machine

// lib/workflow-engine.js
class WorkflowEngine {
  constructor() {
    this.workflows = new Map();
    this.activeRuns = new Map();
  }
  
  async startWorkflow(workflowId, input) {
    const workflow = this.workflows.get(workflowId);
    const runId = generateId();
    
    const state = {
      runId,
      workflowId,
      status: "running",
      currentStep: 0,
      data: { trigger: input },
      history: [],
      startedAt: Date.now()
    };
    
    this.activeRuns.set(runId, state);
    
    try {
      await this.executeSteps(state, workflow.steps);
      state.status = "completed";
    } catch (error) {
      state.status = "failed";
      state.error = error.message;
      await this.handleFailure(state, error);
    }
    
    return state;
  }
  
  async executeSteps(state, steps) {
    for (const step of steps) {
      // Check condition
      if (step.condition && !this.evaluateCondition(step.condition, state.data)) {
        state.history.push({ step: step.id, skipped: true });
        continue;
      }
      
      state.currentStep = step.id;
      const input = this.resolveInput(step.input, state.data);
      
      const result = await this.executeWithRetry(
        step.agent, input, step.retry
      );
      
      state.data[step.id] = result;
      state.history.push({
        step: step.id,
        agent: step.agent,
        duration: result.duration,
        tokens: result.tokens
      });
    }
  }
}

Error Handling Strategies

Retry with Backoff

async executeWithRetry(agentName, input, retryConfig) {
  const maxAttempts = retryConfig?.max_attempts || 1;
  let lastError;
  
  for (let attempt = 1; attempt <= maxAttempts; attempt++) {
    try {
      return await this.agents[agentName].process(input);
    } catch (error) {
      lastError = error;
      if (attempt < maxAttempts) {
        const delay = Math.pow(2, attempt) * 1000; // Exponential backoff
        await sleep(delay);
      }
    }
  }
  
  throw lastError;
}

Fallback Agents

steps:
  - id: summarize
    agent: gpt4_summarizer
    fallback:
      agent: gpt35_summarizer  # Use cheaper model if primary fails
      condition: "timeout or rate_limit"

Circuit Breaker

class CircuitBreaker {
  constructor(threshold = 5, resetTime = 60000) {
    this.failures = 0;
    this.threshold = threshold;
    this.resetTime = resetTime;
    this.state = "closed"; // closed, open, half-open
    this.lastFailure = null;
  }
  
  async execute(fn) {
    if (this.state === "open") {
      if (Date.now() - this.lastFailure > this.resetTime) {
        this.state = "half-open";
      } else {
        throw new Error("Circuit breaker is open");
      }
    }
    
    try {
      const result = await fn();
      this.onSuccess();
      return result;
    } catch (error) {
      this.onFailure();
      throw error;
    }
  }
}

Advanced Patterns

Fan-Out/Fan-In

Process tasks in parallel, then merge results:

async function fanOutFanIn(task, agents) {
  // Fan out: Send to all agents in parallel
  const promises = agents.map(agent => 
    agent.process(task).catch(err => ({ error: err.message }))
  );
  
  const results = await Promise.allSettled(promises);
  
  // Fan in: Merge results
  const successful = results
    .filter(r => r.status === "fulfilled" && !r.value.error)
    .map(r => r.value);
  
  // Use a merger agent to combine perspectives
  return await agents.merger.process({
    task: "Combine these perspectives into a unified answer",
    perspectives: successful
  });
}

Event-Driven Orchestration

class EventOrchestrator {
  constructor() {
    this.handlers = new Map();
  }
  
  on(event, handler) {
    this.handlers.set(event, handler);
  }
  
  async emit(event, data) {
    const handler = this.handlers.get(event);
    if (handler) {
      const result = await handler(data);
      // Chain events
      if (result.nextEvent) {
        await this.emit(result.nextEvent, result.data);
      }
    }
  }
}

// Usage
const orch = new EventOrchestrator();
orch.on("new_email", async (email) => {
  const classification = await agents.classifier.process(email);
  return { nextEvent: `email_${classification.type}`, data: email };
});
orch.on("email_urgent", async (email) => {
  await agents.notifier.process(email);
  await agents.responder.process(email);
});

Monitoring Orchestrated Workflows

monitoring:
  dashboards:
    workflow_overview:
      - active_workflows
      - completed_today
      - failed_today
      - average_duration
      
    agent_performance:
      - response_times_by_agent
      - error_rates_by_agent
      - token_usage_by_agent
      
    cost_tracking:
      - daily_cost_by_workflow
      - cost_per_completion
      - projected_monthly_cost

Best Practices

• Define clear workflow boundaries — each workflow should have a single purpose
• Implement timeouts at every level — step, workflow, and global
• Use idempotent operations — safe to retry without side effects
• Log all state transitions — essential for debugging
• Start simple — begin with linear pipelines before adding branching
• Test workflows end-to-end — unit testing agents is not enough
• Plan for partial failures — some steps failing should not crash everything
• Version your workflows — changes should be backward compatible