Purpose: Integrate DSPy.ts optimization capabilities with Claude-Flow swarm orchestration for self-improving multi-agent systems.
┌─────────────────────────────────────────────────────────────┐
│ Claude-Flow Layer │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ Swarm │ │ Memory │ │ Neural │ │
│ │Coordinator │ │ Manager │ │ Training │ │
│ └─────┬──────┘ └─────┬──────┘ └─────┬──────┘ │
│ │ │ │ │
└────────┼────────────────┼────────────────┼───────────────────┘
│ │ │
┌────────┼────────────────┼────────────────┼───────────────────┐
│ ▼ ▼ ▼ │
│ DSPy.ts Layer │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ Signature │ │ Optimizer │ │ Program │ │
│ │ Builder │ │ (GEPA) │ │ Compiler │ │
│ └─────┬──────┘ └─────┬──────┘ └─────┬──────┘ │
│ │ │ │ │
└────────┼────────────────┼────────────────┼───────────────────┘
│ │ │
┌────────┼────────────────┼────────────────┼───────────────────┐
│ ▼ ▼ ▼ │
│ LLM Provider Layer │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ Claude │ │ GPT-4 │ │ OpenRouter │ │
│ │ 3.5 Sonnet │ │ Turbo │ │ (Llama) │ │
│ └────────────┘ └────────────┘ └────────────┘ │
└─────────────────────────────────────────────────────────────┘
# Core dependencies
npm install @ax-llm/ax
npm install claude-flow@alpha
# Optional: Enhanced coordination
npm install ruv-swarm
npm install reasoning-bank
# Optional: Cloud features
npm install flow-nexus@latest// src/integrations/claude-flow-dspy.ts
import { SwarmOrchestrator } from 'claude-flow';
import { ai, ax } from '@ax-llm/ax';
import { GEPA, MIPROv2 } from '@ax-llm/ax/optimizers';
export class ClaudeFlowDSPy {
private swarm: SwarmOrchestrator;
private models: Map<string, any>;
private optimizedAgents: Map<string, any>;
constructor() {
this.swarm = new SwarmOrchestrator({
topology: 'adaptive',
maxAgents: 10
});
this.models = new Map([
['primary', ai({
name: 'anthropic',
apiKey: process.env.ANTHROPIC_API_KEY,
model: 'claude-3-5-sonnet-20241022'
})],
['fallback', ai({
name: 'openai',
apiKey: process.env.OPENAI_API_KEY,
model: 'gpt-4-turbo'
})],
['cost-effective', ai({
name: 'openrouter',
apiKey: process.env.OPENROUTER_API_KEY,
model: 'meta-llama/llama-3.1-70b-instruct'
})]
]);
this.optimizedAgents = new Map();
}
/**
* Create and optimize an agent with DSPy.ts
*/
async createOptimizedAgent(
agentType: string,
signature: string,
trainset: any[],
options = {}
) {
// 1. Create base DSPy program
const program = ax(signature);
// 2. Define evaluation metric
const metric = options.metric || this.defaultMetric;
// 3. Select optimizer based on dataset size
const optimizer = this.selectOptimizer(trainset.length, metric);
// 4. Compile optimized program
console.log(`Optimizing ${agentType} agent...`);
const optimized = await optimizer.compile(program, trainset);
// 5. Store in Claude-Flow swarm
const agent = await this.swarm.createAgent({
type: agentType,
handler: async (input) => {
const model = this.selectModel(input);
return optimized.forward(model, input);
},
metadata: {
signature,
optimizer: optimizer.constructor.name,
trainedAt: new Date().toISOString(),
datasetSize: trainset.length
}
});
this.optimizedAgents.set(agentType, { program: optimized, agent });
return agent;
}
/**
* Select appropriate optimizer based on dataset size
*/
private selectOptimizer(datasetSize: number, metric: any) {
if (datasetSize < 20) {
return new BootstrapFewShot({ metric, maxBootstrappedDemos: 4 });
} else if (datasetSize < 100) {
return new BootstrapFewShot({
metric,
maxBootstrappedDemos: 8,
maxRounds: 2
});
} else {
return new MIPROv2({
metric,
numCandidates: 10,
numTrials: 100
});
}
}
/**
* Select model based on input complexity
*/
private selectModel(input: any): any {
const complexity = this.analyzeComplexity(input);
if (complexity < 0.3) return this.models.get('cost-effective');
if (complexity < 0.7) return this.models.get('fallback');
return this.models.get('primary');
}
/**
* Analyze input complexity (simple heuristic)
*/
private analyzeComplexity(input: any): number {
const text = JSON.stringify(input);
const length = text.length;
const hasCode = /```|function|class|import/.test(text);
const hasMultipleQuestions = (text.match(/\?/g) || []).length > 2;
let complexity = Math.min(length / 1000, 0.5);
if (hasCode) complexity += 0.3;
if (hasMultipleQuestions) complexity += 0.2;
return Math.min(complexity, 1.0);
}
/**
* Default metric for optimization
*/
private defaultMetric(example: any, prediction: any): number {
// Simple exact match
return prediction.output === example.output ? 1.0 : 0.0;
}
}// src/agents/researcher-agent.ts
import { ClaudeFlowDSPy } from '../integrations/claude-flow-dspy';
export async function createResearcherAgent(cfDspy: ClaudeFlowDSPy) {
const signature = `
query:string,
context:string[]
->
findings:string,
sources:string[],
confidence:number
`;
const trainset = [
{
query: "What are the latest developments in AI?",
context: ["Article 1 about GPT-4", "Article 2 about Claude"],
findings: "Recent AI developments include...",
sources: ["GPT-4 paper", "Claude 3 announcement"],
confidence: 0.9
},
// ... 20-50 more examples
];
const metric = (example, prediction) => {
const findingsMatch = prediction.findings.length > 50 ? 0.5 : 0;
const sourcesMatch = prediction.sources.length > 0 ? 0.3 : 0;
const confidenceMatch = prediction.confidence > 0.7 ? 0.2 : 0;
return findingsMatch + sourcesMatch + confidenceMatch;
};
return cfDspy.createOptimizedAgent(
'researcher',
signature,
trainset,
{ metric }
);
}// src/agents/coder-agent.ts
export async function createCoderAgent(cfDspy: ClaudeFlowDSPy) {
const signature = `
description:string,
language:class "typescript, python, rust, go",
requirements:string[]
->
code:string,
tests:string,
documentation:string
`;
const trainset = [
{
description: "REST API endpoint for user authentication",
language: "typescript",
requirements: ["JWT tokens", "bcrypt password hashing"],
code: "// Express endpoint code...",
tests: "// Jest test suite...",
documentation: "// API documentation..."
},
// ... more examples
];
const metric = (example, prediction) => {
const hasCode = prediction.code.length > 100 ? 0.4 : 0;
const hasTests = prediction.tests.length > 50 ? 0.3 : 0;
const hasDocs = prediction.documentation.length > 20 ? 0.3 : 0;
return hasCode + hasTests + hasDocs;
};
return cfDspy.createOptimizedAgent(
'coder',
signature,
trainset,
{ metric }
);
}// src/agents/tester-agent.ts
export async function createTesterAgent(cfDspy: ClaudeFlowDSPy) {
const signature = `
code:string,
language:class "typescript, python, rust, go",
requirements:string[]
->
tests:string,
coverage:number,
edge_cases:string[]
`;
const trainset = [
{
code: "function add(a, b) { return a + b; }",
language: "typescript",
requirements: ["Test positive numbers", "Test negative numbers"],
tests: "describe('add', () => { ... })",
coverage: 0.95,
edge_cases: ["NaN handling", "Infinity"]
},
// ... more examples
];
const metric = (example, prediction) => {
const hasTests = prediction.tests.length > 100 ? 0.4 : 0;
const goodCoverage = prediction.coverage > 0.8 ? 0.3 : 0;
const hasEdgeCases = prediction.edge_cases.length > 2 ? 0.3 : 0;
return hasTests + goodCoverage + hasEdgeCases;
};
return cfDspy.createOptimizedAgent(
'tester',
signature,
trainset,
{ metric }
);
}// src/workflows/feature-development.ts
import { ClaudeFlowDSPy } from '../integrations/claude-flow-dspy';
import { createResearcherAgent } from '../agents/researcher-agent';
import { createCoderAgent } from '../agents/coder-agent';
import { createTesterAgent } from '../agents/tester-agent';
export class FeatureDevelopmentWorkflow {
private cfDspy: ClaudeFlowDSPy;
private agents: Map<string, any>;
constructor() {
this.cfDspy = new ClaudeFlowDSPy();
this.agents = new Map();
}
async initialize() {
// Create optimized agents in parallel
const [researcher, coder, tester] = await Promise.all([
createResearcherAgent(this.cfDspy),
createCoderAgent(this.cfDspy),
createTesterAgent(this.cfDspy)
]);
this.agents.set('researcher', researcher);
this.agents.set('coder', coder);
this.agents.set('tester', tester);
console.log('✅ All agents optimized and ready');
}
async developFeature(featureRequest: string) {
// Step 1: Research
const researchResult = await this.agents.get('researcher').execute({
query: featureRequest,
context: await this.gatherContext(featureRequest)
});
console.log('📊 Research complete:', researchResult.findings);
// Step 2: Code
const codeResult = await this.agents.get('coder').execute({
description: featureRequest,
language: 'typescript',
requirements: this.extractRequirements(researchResult)
});
console.log('💻 Code generated:', codeResult.code.substring(0, 100) + '...');
// Step 3: Test
const testResult = await this.agents.get('tester').execute({
code: codeResult.code,
language: 'typescript',
requirements: this.extractRequirements(researchResult)
});
console.log('✅ Tests generated:', testResult.coverage);
return {
research: researchResult,
code: codeResult,
tests: testResult,
complete: testResult.coverage > 0.8
};
}
private async gatherContext(query: string): Promise<string[]> {
// Implement context gathering (e.g., from documentation, codebase)
return [];
}
private extractRequirements(research: any): string[] {
// Extract requirements from research findings
return [];
}
}// src/integrations/reasoning-bank-dspy.ts
import { ReasoningBank } from 'reasoning-bank';
import { ClaudeFlowDSPy } from './claude-flow-dspy';
export class SelfLearningOrchestrator {
private cfDspy: ClaudeFlowDSPy;
private reasoningBank: ReasoningBank;
constructor() {
this.cfDspy = new ClaudeFlowDSPy();
this.reasoningBank = new ReasoningBank({
storageBackend: 'agentdb', // 150x faster vector search
learningEnabled: true
});
}
/**
* Create agent that learns from production
*/
async createSelfLearningAgent(agentType: string, signature: string) {
// 1. Check if we have prior training data
const priorData = await this.reasoningBank.query({
agentType,
signature,
limit: 100
});
// 2. Create or update optimized agent
let agent;
if (priorData.length > 20) {
console.log(`📚 Found ${priorData.length} prior examples, optimizing...`);
agent = await this.cfDspy.createOptimizedAgent(
agentType,
signature,
priorData,
{
metric: this.computeMetricFromFeedback
}
);
} else {
console.log('🆕 Creating new agent with baseline');
agent = await this.cfDspy.createOptimizedAgent(
agentType,
signature,
this.getBaselineExamples(agentType)
);
}
// 3. Wrap agent to learn from production
return this.wrapWithLearning(agent, agentType, signature);
}
/**
* Wrap agent to capture production data for learning
*/
private wrapWithLearning(agent: any, agentType: string, signature: string) {
return {
async execute(input: any) {
const startTime = Date.now();
// Execute agent
const result = await agent.execute(input);
// Store in ReasoningBank
await this.reasoningBank.store({
agentType,
signature,
input,
output: result,
latency: Date.now() - startTime,
timestamp: new Date(),
metadata: {
model: 'optimized',
version: agent.metadata?.trainedAt
}
});
return result;
},
/**
* Re-optimize based on production data
*/
async reoptimize() {
// Get recent production data
const productionData = await this.reasoningBank.query({
agentType,
signature,
since: Date.now() - 7 * 24 * 60 * 60 * 1000, // Last 7 days
minQuality: 0.8 // Only good examples
});
if (productionData.length < 10) {
console.log('⚠️ Not enough production data for reoptimization');
return agent;
}
console.log(`🔄 Reoptimizing with ${productionData.length} production examples...`);
// Create new optimized version
const newAgent = await this.cfDspy.createOptimizedAgent(
agentType,
signature,
productionData,
{
metric: this.computeMetricFromFeedback
}
);
// Compare performance
const oldPerf = await this.evaluateAgent(agent, productionData.slice(0, 20));
const newPerf = await this.evaluateAgent(newAgent, productionData.slice(0, 20));
if (newPerf > oldPerf) {
console.log(`✅ Improved performance: ${oldPerf.toFixed(2)} → ${newPerf.toFixed(2)}`);
return this.wrapWithLearning(newAgent, agentType, signature);
} else {
console.log(`⚠️ No improvement, keeping current version`);
return agent;
}
}
};
}
private async evaluateAgent(agent: any, testData: any[]): Promise<number> {
const scores = await Promise.all(
testData.map(async (example) => {
const prediction = await agent.execute(example.input);
return this.computeMetricFromFeedback(example, prediction);
})
);
return scores.reduce((a, b) => a + b, 0) / scores.length;
}
private computeMetricFromFeedback(example: any, prediction: any): number {
// Compute quality score based on feedback
const hasOutput = prediction.output ? 0.3 : 0;
const hasQuality = prediction.quality > 0.7 ? 0.4 : 0;
const hasFeedback = example.feedback === 'positive' ? 0.3 : 0;
return hasOutput + hasQuality + hasFeedback;
}
private getBaselineExamples(agentType: string): any[] {
// Return baseline training examples for new agents
return [];
}
}// src/monitoring/dspy-metrics.ts
import { trace, context } from '@opentelemetry/api';
export class DSPyMetricsCollector {
private tracer = trace.getTracer('dspy-metrics');
async trackOptimization(agentType: string, fn: () => Promise<any>) {
const span = this.tracer.startSpan('dspy-optimization');
span.setAttributes({
'dspy.agent_type': agentType,
'dspy.phase': 'optimization'
});
const startTime = Date.now();
try {
const result = await fn();
span.setAttributes({
'dspy.optimization_time': Date.now() - startTime,
'dspy.success': true
});
return result;
} catch (error) {
span.recordException(error);
span.setAttributes({
'dspy.success': false,
'dspy.error': error.message
});
throw error;
} finally {
span.end();
}
}
async trackInference(agentType: string, fn: () => Promise<any>) {
const span = this.tracer.startSpan('dspy-inference');
span.setAttributes({
'dspy.agent_type': agentType,
'dspy.phase': 'inference'
});
const startTime = Date.now();
try {
const result = await fn();
span.setAttributes({
'dspy.latency': Date.now() - startTime,
'dspy.tokens.input': result.usage?.inputTokens || 0,
'dspy.tokens.output': result.usage?.outputTokens || 0,
'dspy.success': true
});
return result;
} catch (error) {
span.recordException(error);
span.setAttributes({
'dspy.success': false,
'dspy.error': error.message
});
throw error;
} finally {
span.end();
}
}
async trackAgentPerformance(
agentType: string,
metric: (ex: any, pred: any) => number,
examples: any[]
) {
const scores = examples.map(({ example, prediction }) =>
metric(example, prediction)
);
const avgScore = scores.reduce((a, b) => a + b, 0) / scores.length;
const stdDev = Math.sqrt(
scores.reduce((sum, s) => sum + Math.pow(s - avgScore, 2), 0) / scores.length
);
// Log metrics
console.log(`📊 ${agentType} Performance:`, {
mean: avgScore.toFixed(3),
stdDev: stdDev.toFixed(3),
min: Math.min(...scores).toFixed(3),
max: Math.max(...scores).toFixed(3)
});
return {
agentType,
mean: avgScore,
stdDev,
min: Math.min(...scores),
max: Math.max(...scores),
samples: examples.length
};
}
}// examples/self-improving-docs-generator.ts
import { ClaudeFlowDSPy } from '../src/integrations/claude-flow-dspy';
import { SelfLearningOrchestrator } from '../src/integrations/reasoning-bank-dspy';
async function main() {
const orchestrator = new SelfLearningOrchestrator();
// Create self-learning documentation agent
const docsAgent = await orchestrator.createSelfLearningAgent(
'docs-generator',
`
code:string,
language:class "typescript, python, rust",
style:class "technical, beginner-friendly, api-reference"
->
documentation:string,
examples:string[],
quality_score:number
`
);
// Use agent
const result = await docsAgent.execute({
code: `
function calculateFibonacci(n: number): number {
if (n <= 1) return n;
return calculateFibonacci(n - 1) + calculateFibonacci(n - 2);
}
`,
language: 'typescript',
style: 'beginner-friendly'
});
console.log('📝 Generated Documentation:');
console.log(result.documentation);
console.log('\n💡 Examples:');
result.examples.forEach(ex => console.log(' -', ex));
console.log(`\n✨ Quality Score: ${result.quality_score}`);
// Simulate production usage for 1 week...
// Agent automatically learns from good examples
// Re-optimize weekly
setInterval(async () => {
console.log('\n🔄 Weekly reoptimization...');
await docsAgent.reoptimize();
}, 7 * 24 * 60 * 60 * 1000);
}
main().catch(console.error);- Install Ax framework and Claude-Flow
- Configure API keys for Claude, GPT-4, OpenRouter
- Set up basic ClaudeFlowDSPy class
- Test basic agent creation
- Create researcher agent with training data
- Create coder agent with training data
- Create tester agent with training data
- Test agents individually
- Collect 20-50 training examples per agent
- Run BootstrapFewShot optimization
- Evaluate performance improvements
- Document baseline vs optimized metrics
- Integrate with ReasoningBank for learning
- Set up production monitoring
- Implement model cascading
- Add caching layer
- Deploy optimized agents
- Monitor performance metrics
- Collect production feedback
- Schedule weekly reoptimization
-
Start with BootstrapFewShot
- Faster optimization (15 min vs 2 hours)
- Good enough for most use cases
- Upgrade to MIPROv2/GEPA later if needed
-
Use Model Cascading
- Cheap model (Llama 3.1 8B) for simple tasks
- Medium model (Claude Haiku) for moderate tasks
- Expensive model (Claude Sonnet) for complex tasks
- Can reduce costs by 60-80%
-
Implement Continuous Learning
- Store all production interactions in ReasoningBank
- Filter for high-quality examples (quality > 0.8)
- Reoptimize weekly with production data
- Track performance improvements over time
-
Monitor Everything
- Track optimization time and cost
- Monitor inference latency and cost
- Log all predictions for analysis
- Set up alerts for performance degradation
-
Version Control Optimized Agents
- Save optimized programs to disk
- Track training date and dataset size
- A/B test new versions before deploying
- Keep rollback capability
- Accuracy: +15-30% with BootstrapFewShot
- Accuracy: +30-50% with MIPROv2
- Accuracy: +40-60% with GEPA
- Cost: 22-90x reduction with GEPA optimization
- Self-improving agents learn from production data
- Reduced latency through model cascading
- Lower costs through optimization and caching
- Better quality through continuous learning
- Comprehensive Research: See
docs/research/dspy-ts-comprehensive-research.md - Quick Start: See
docs/research/dspy-ts-quick-start-guide.md - Ax Documentation: https://axllm.dev/
- Claude-Flow Docs: https://github.com/ruvnet/claude-flow
Integration Guide Created By: Research Agent Last Updated: 2025-11-22 Status: Ready for Implementation