@nahisaho/musubix-deep-research
v3.8.2
Published
Deep Research Integration - Iterative search-read-reason cycle for AI agents
Maintainers
Readme
@nahisaho/musubix-deep-research
Deep Research Integration for MUSUBIX - Iterative search-read-reason cycle for AI agents during requirements and design phases.
Features
- 🔄 Iterative Research Cycle: Search → Read → Reason → Reflect loop
- 🔍 Multi-Provider Search: Jina AI (primary), Brave (fallback 1), DuckDuckGo (fallback 2)
- 🧠 LM API Integration: VS Code LM API (GitHub Copilot) for reasoning
- 📚 Knowledge Accumulation: Persistent knowledge base across iterations
- 💰 Token Budget Management: Automatic tracking and limits
- 📊 Research Reports: Markdown/JSON formatted reports with citations
Installation
npm install @nahisaho/musubix-deep-researchUsage
CLI
# Start deep research
npx musubix deep-research "How to implement authentication in TypeScript?"
# With options
npx musubix deep-research "TypeScript decorators" \
--max-iterations 5 \
--token-budget 10000 \
--output report.mdProgrammatic API
import { ResearchEngine, ResearchConfig } from '@nahisaho/musubix-deep-research';
const config: ResearchConfig = {
query: 'How to implement authentication in TypeScript?',
maxIterations: 10,
tokenBudget: 15000,
providers: {
jinaApiKey: process.env.JINA_API_KEY,
braveApiKey: process.env.BRAVE_API_KEY,
},
};
const engine = new ResearchEngine(config);
const report = await engine.research();
console.log(report.summary);
console.log(`Found ${report.findings.length} findings`);Architecture
Based on Template Method Pattern (ADR-v3.4.0-001):
ResearchEngine (Template Method)
├─ initialize()
├─ while (!shouldStop())
│ ├─ generateQuestions() → LMReasoning
│ ├─ search() → SearchProviderFactory
│ ├─ read() → JinaProvider.read()
│ ├─ reason() → LMReasoning
│ └─ logIteration() → TrajectoryLogger
└─ generateReport() → ReportGeneratorRequirements
- Node.js >= 20.0.0
- npm >= 10.0.0
- VS Code with GitHub Copilot (for LM API)
- Jina AI API Key (recommended)
License
MIT
