Workflow Automation by spencermarx

Overview

Integrate AI swarms with GitHub Actions to create intelligent, self-organizing CI/CD pipelines that adapt to your codebase through advanced multi-agent coordination and automation, enhanced with self-learning and continuous improvement capabilities powered by Agentic-Flow v3.0.0-alpha.1.

🧠 Self-Learning Protocol (v3.0.0-alpha.1)

Before Workflow Creation: Learn from Past Workflows

// 1. Search for similar past workflows
const similarWorkflows = await reasoningBank.searchPatterns({
  task: `CI/CD workflow for ${repoType}`,
  k: 5,
  minReward: 0.8
});

if (similarWorkflows.length > 0) {
  console.log('📚 Learning from past successful workflows:');
  similarWorkflows.forEach(pattern => {
    console.log(`- ${pattern.task}: ${pattern.reward} success rate`);
    console.log(`  Workflow strategy: ${pattern.output.strategy}`);
    console.log(`  Average runtime: ${pattern.output.avgRuntime}ms`);
    console.log(`  Success rate: ${pattern.output.successRate}%`);
  });
}

// 2. Learn from workflow failures
…

During Workflow Execution: GNN-Enhanced Optimization

// Build workflow dependency graph
const buildWorkflowGraph = (jobs) => ({
  nodes: jobs.map(j => ({ id: j.name, type: j.type })),
  edges: analyzeJobDependencies(jobs),
  edgeWeights: calculateJobDurations(jobs),
  nodeLabels: jobs.map(j => j.name)
});

// GNN-enhanced workflow optimization (+12.4% better)
const optimizations = await agentDB.gnnEnhancedSearch(
  workflowEmbedding,
  {
    k: 10,
    graphContext: buildWorkflowGraph(workflowJobs),
    gnnLayers: 3
  }
);
…

Multi-Agent Workflow Optimization with Attention

// Coordinate optimization decisions using attention consensus
const coordinator = new AttentionCoordinator(attentionService);

const optimizationProposals = [
  { agent: 'cache-optimizer', proposal: 'add-dependency-caching', impact: 0.45 },
  { agent: 'parallel-optimizer', proposal: 'parallelize-tests', impact: 0.60 },
  { agent: 'resource-optimizer', proposal: 'upgrade-runners', impact: 0.30 },
  { agent: 'security-optimizer', proposal: 'add-security-scan', impact: 0.85 }
];

const consensus = await coordinator.coordinateAgents(
  optimizationProposals,
  'moe' // Mixture of Experts routing
);

console.log(`Optimization consensus: ${consensus.topOptimizations}`);
console.log(`Expected improvement: ${consensus.totalImpact}%`);
console.log(`Agent influence: ${consensus.attentionWeights}`);
…

After Workflow Run: Store Learning Patterns

// Store workflow performance pattern
const workflowMetrics = {
  totalRuntime: endTime - startTime,
  jobsCount: jobs.length,
  successRate: passedJobs / totalJobs,
  cacheHitRate: cacheHits / cacheMisses,
  parallelizationScore: parallelJobs / totalJobs,
  costPerRun: calculateCost(runtime, runnerSize),
  failureRate: failedJobs / totalJobs,
  bottlenecks: identifiedBottlenecks
};

await reasoningBank.storePattern({
  sessionId: `workflow-${workflowId}-${Date.now()}`,
  task: `CI/CD workflow for ${repo.name}`,
  input: JSON.stringify({ repo, triggers, jobs }),
  output: JSON.stringify({
    optimizations: appliedOptimizations,
…

🎯 GitHub-Specific Optimizations

Pattern-Based Workflow Generation

// Learn optimal workflow patterns from history
const workflowPatterns = await reasoningBank.searchPatterns({
  task: 'workflow generation',
  k: 50,
  minReward: 0.85
});

const optimalWorkflow = generateWorkflowFromPatterns(workflowPatterns, repoContext);

// Returns optimized YAML based on learned patterns
console.log(`Generated workflow with ${optimalWorkflow.optimizationScore}% efficiency`);

Attention-Based Job Prioritization

// Use Flash Attention to prioritize critical jobs
const jobPriorities = await agentDB.flashAttention(
  jobEmbeddings,
  criticalityEmbeddings,
  criticalityEmbeddings
);

// Reorder workflow for optimal execution
const optimizedJobOrder = jobs.sort((a, b) =>
  jobPriorities[b.id] - jobPriorities[a.id]
);

console.log(`Job prioritization completed in ${processingTime}ms (2.49x-7.47x faster)`);

GNN-Enhanced Failure Prediction

// Build historical failure graph
const failureGraph = {
  nodes: pastWorkflowRuns,
  edges: buildFailureCorrelations(),
  edgeWeights: calculateFailureProbabilities(),
  nodeLabels: pastWorkflowRuns.map(r => `run-${r.id}`)
};

// Predict potential failures with GNN
const riskAnalysis = await agentDB.gnnEnhancedSearch(
  currentWorkflowEmbedding,
  {
    k: 10,
    graphContext: failureGraph,
    gnnLayers: 3,
    filter: 'failed_runs'
  }
);
…

Adaptive Workflow Learning

// Continuous learning from workflow executions
const performanceTrends = await reasoningBank.getPatternStats({
  task: 'workflow execution',
  k: 100
});

console.log(`Performance improvement over time: ${performanceTrends.improvementPercent}%`);
console.log(`Common optimizations: ${performanceTrends.commonPatterns}`);
console.log(`Best practices emerged: ${performanceTrends.bestPractices}`);

// Auto-apply learned optimizations
if (performanceTrends.improvementPercent > 10) {
  await applyLearnedOptimizations(performanceTrends.bestPractices);
}

Core Features

1. Swarm-Powered Actions

# .github/workflows/swarm-ci.yml
name: Intelligent CI with Swarms
on: [push, pull_request]

jobs:
  swarm-analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Initialize Swarm
        uses: ruvnet/swarm-action@v1
        with:
          topology: mesh
          max-agents: 6
          
      - name: Analyze Changes
        run: |
…

2. Dynamic Workflow Generation

# Generate workflows based on code analysis
npx claude-flow@v3alpha actions generate-workflow \
  --analyze-codebase \
  --detect-languages \
  --create-optimal-pipeline