This SDK gives JavaScript developers full access to advanced AI features like agent orchestration, RAG, and fine-tuning — without needing to manage backend code.

The MultiMind SDK provides a comprehensive JavaScript interface for advanced AI capabilities:

• 🤖 AI Agent Orchestration: Create intelligent agents that can reason, plan, and execute complex tasks
• 🔍 RAG (Retrieval-Augmented Generation): Build knowledge systems that combine your data with AI reasoning
• 🎯 Model Fine-tuning: Customize AI models for your specific use cases and domains
• 🔄 Model Routing: Automatically select the best AI model for each task
• 📊 Model Evaluation: Assess and compare AI model performance
• 🔧 Adapter Management: Enhance models with specialized capabilities
• 🛠️ Advanced Workflows: LoRA fine-tuning, document processing, compliance monitoring, and more

npm install multimind-sdk

import { MultiMindSDK } from 'multimind-sdk'; async function main() { const sdk = new MultiMindSDK(); try { await sdk.initialize(); // Generate a response with an AI agent const response = await sdk.generateWithAgent( "What is artificial intelligence?", { model: "mistral", temperature: 0.7 } ); console.log(response); } finally { await sdk.close(); } } main();

import { MultiMindSDK } from 'multimind-sdk'; const sdk = new MultiMindSDK(); // Advanced Fine-tuning with LoRA const fineTuneResult = await sdk.advancedFineTune({ baseModelName: "bert-base-uncased", outputDir: "./output", method: "lora", epochs: 10, learningRate: 0.001, batchSize: 32, loraConfig: { r: 16, alpha: 32, dropout: 0.1, targetModules: ["query", "value"] } }); // Advanced RAG with Document Management const documents = [ { text: "MultiMind SDK provides comprehensive AI capabilities.", metadata: { type: "introduction", source: "docs" } } ]; await sdk.addDocumentsToRAG(documents); const ragResponse = await sdk.queryAdvancedRAG({ query: "What is MultiMind SDK?", topK: 5, includeMetadata: true }); // Model Conversion const conversionResult = await sdk.pytorchToONNX( "./models/model.pt", "./models/model.onnx", { quantization: { method: "int8", targetDevice: "cpu" }, graphOptimization: { fuseOperations: true, optimizeMemory: true } } ); // Compliance Monitoring const complianceResult = await sdk.checkCompliance({ modelId: "model_123", dataCategories: ["text", "user_data"], useCase: "customer_support", region: "EU" }); // Advanced Agent with Tools const agentResponse = await sdk.runAdvancedAgent( "Calculate 15 * 23 and search for quantum computing information", { context: "mathematical and scientific inquiry" } ); // Model Client System const lstmClient = await sdk.createLSTMModelClient({ modelPath: "./models/lstm.pt", modelName: "custom_lstm" }); const moeClient = await sdk.createMoEModelClient({ experts: { "expert1": { modelName: "gpt-3.5-turbo" }, "expert2": { modelName: "claude-3" } }, router: (input: string) => input.length > 100 ? "expert2" : "expert1" }); // Gateway API const gateway = await sdk.startGateway({ host: "0.0.0.0", port: 8000, enableMiddleware: true, corsEnabled: true, rateLimit: 100 });

• OpenAI Models: GPT-3.5, GPT-4, GPT-4 Turbo
• Anthropic Models: Claude-2, Claude-3, Claude-3.5 Sonnet
• Open Source Models: Mistral, Llama, BERT, and many more
• Custom Models: Load and use your own fine-tuned models

• Basic Agents: Simple question-answering and text generation
• Advanced Agents: Multi-step reasoning, tool usage, and memory
• Specialized Agents: Code generation, data analysis, creative writing
• Custom Agents: Build agents tailored to your specific domain

• Document Processing: PDF, DOCX, TXT, and more
• Vector Storage: Efficient similarity search
• Knowledge Graphs: Structured information retrieval
• Hybrid Search: Combine semantic and keyword search

The MultiMind SDK includes a powerful CLI for automation and batch operations:

npm install -g multimind-sdk

# Transfer context between different AI models multimind-cli --source chatgpt --target claude --input conversation.json --output prompt.txt # List supported models multimind-cli --list-models # Run batch transfer operations multimind-cli --batch # Generate Chrome extension configuration multimind-cli --chrome-config

# Basic Transfer --source, -s <model> Source model name (e.g., chatgpt, claude) --target, -t <model> Target model name (e.g., deepseek, gemini) --input, -i <file> Input conversation file (JSON, TXT, MD) --output, -o <file> Output formatted prompt file # Transfer Options --last-n <number> Number of recent messages to extract (default: 5) --summary-type <type> Summary type: concise, detailed, structured --output-format <format> Output format: txt, json, markdown --no-smart-extraction Disable smart context extraction --no-metadata Exclude metadata from output # Model-Specific Options --include-code Include code context (for coding models) --include-reasoning Include reasoning capabilities --include-safety Include safety considerations --include-creativity Include creative capabilities --include-examples Include example generation --include-step-by-step Include step-by-step explanations --include-multimodal Include multimodal capabilities --include-web-search Include web search capabilities # Advanced Features --batch Run batch transfer operations --validate Validate conversation format --list-models List all supported models --chrome-config Generate Chrome extension configuration --help, -h Show help message

# Basic transfer from ChatGPT to Claude multimind-cli --source chatgpt --target claude --input conversation.json --output prompt.txt # Advanced transfer with custom options multimind-cli --source gpt-4 --target deepseek --input chat.txt --output formatted.md \ --summary-type detailed --include-code --include-reasoning # Batch transfer with validation multimind-cli --batch --validate # Generate Chrome extension config multimind-cli --chrome-config

// Automated content generation const sdk = new MultiMindSDK(); await sdk.initialize(); // Generate blog posts from outlines const outline = "AI trends in 2024"; const blogPost = await sdk.generateWithAgent( `Write a comprehensive blog post about: ${outline}`, { model: "gpt-4", temperature: 0.7 } ); // Automated customer support const customerQuery = "How do I reset my password?"; const response = await sdk.queryAdvancedRAG({ query: customerQuery, topK: 3, includeMetadata: true });

# GitHub Actions example - name: Generate Documentation run: |  npm install multimind-sdk  node scripts/generate-docs.js

// Process multiple documents const documents = await loadDocuments('./data/'); const results = []; for (const doc of documents) { const summary = await sdk.generateWithAgent( `Summarize this document: ${doc.content}`, { model: "claude-3" } ); results.push({ id: doc.id, summary }); }

# Basic agent example npm run example:agent # Advanced usage example npm run example:advanced # Comprehensive demo npm run demo # CLI directly npm run cli -- --help

• example/run-agent.ts: Basic agent generation example
• example/advanced-usage.ts: Advanced features example
• example/comprehensive-demo.ts: Complete feature demonstration
• example/context-transfer-cli.ts: CLI implementation

The main SDK class that provides a unified interface to all MultiMind functionality.

• generateWithAgent(prompt: string, config?: AgentConfig): Generate responses using AI agents
• createAgent(config?: AgentConfig): Create a new agent instance

• fineTuneModel(config: FineTuneConfig): Fine-tune a model
• createFineTuner(config: FineTuneConfig): Create a fine-tuner instance

• queryRAG(prompt: string, config: RAGConfig): Query a RAG system
• createRAGEngine(config: RAGConfig): Create a RAG engine instance

• loadAdapter(config: AdapterConfig): Load a model adapter
• listAdapters(model: string): List available adapters for a model
• removeAdapter(model: string, adapterPath: string): Remove an adapter

• evaluateModel(config: EvaluationConfig): Evaluate a model
• compareModels(models: string[], task: string, dataset?: string): Compare multiple models

• loadModel(config: ModelConfig): Load a model
• routeModel(input: string, availableModels?: string[]): Route to the best model
• listAvailableModels(): List all available models

• advancedFineTune(config: AdvancedFineTuneConfig): Advanced fine-tuning with LoRA, Adapters, etc.
• createAdvancedTuner(config: AdvancedFineTuneConfig): Create advanced tuner

• createAdvancedRAG(config?: AdvancedRAGConfig): Create advanced RAG client
• addDocumentsToRAG(documents: Document[]): Add documents to RAG
• queryAdvancedRAG(config: QueryConfig): Query advanced RAG system

• createModelConverter(): Create model converter
• convertModel(config: ConversionConfig): Convert model between formats
• pytorchToONNX(inputPath: string, outputPath: string, config?): Convert PyTorch to ONNX
• tensorflowToTFLite(inputPath: string, outputPath: string, config?): Convert TensorFlow to TFLite
• pytorchToGGUF(inputPath: string, outputPath: string, config?): Convert PyTorch to GGUF

• createComplianceMonitor(config: ComplianceConfig): Create compliance monitor
• checkCompliance(check: ComplianceCheck): Check compliance

• createAdvancedAgent(config: AdvancedAgentConfig): Create advanced agent
• runAdvancedAgent(input: string, context?): Run advanced agent with tools

• createLSTMModelClient(config: ModelClientConfig): Create LSTM model client
• createMoEModelClient(config: MoEConfig): Create MoE model client
• createMultiModalClient(config: MultiModalConfig): Create MultiModal client
• createFederatedRouter(config: FederatedConfig): Create federated router

• createGateway(config?: GatewayConfig): Create gateway
• startGateway(config?: GatewayConfig): Start gateway API
• stopGateway(): Stop gateway

• transferContext(sourceModel: string, targetModel: string, conversationData: ConversationMessage[], options?: TransferOptions): Transfer context between models
• quickTransfer(sourceModel: string, targetModel: string, conversationData: ConversationMessage[], options?: Record<string, any>): Quick context transfer
• getSupportedModels(): Get supported models for context transfer
• validateConversationFormat(data: ConversationMessage[]): Validate conversation format
• batchTransfer(transfers: Array<{sourceModel: string, targetModel: string, conversationData: ConversationMessage[], options?: TransferOptions}>): Batch context transfer
• createChromeExtensionConfig(): Create Chrome extension configuration

• getSDKInfo(): Get SDK information
• healthCheck(): Check SDK health
• initialize(): Initialize SDK
• close(): Close SDK

# Clone the repository git clone <repository-url> cd multimind-sdk # Install dependencies npm install # Build the project npm run build:examples # Run tests npm test # Run linting npm run lint

npm run build # Build TypeScript to JavaScript npm run build:examples # Build with example fixes npm run test # Run test suite npm run test:watch # Run tests in watch mode npm run test:coverage # Run tests with coverage npm run lint # Run ESLint npm run lint:fix # Fix linting issues npm run sync-features # Sync with backend features npm run cli # Run CLI npm run demo # Run comprehensive demo

multimind-sdk/ ├── src/ │ ├── bridge/ │ │ └── multimind-bridge.ts # Backend bridge setup │ ├── agent.ts # Basic agent functionality │ ├── fineTune.ts # Basic fine-tuning functionality │ ├── rag.ts # Basic RAG functionality │ ├── adapters.ts # Adapter management │ ├── evaluation.ts # Model evaluation │ ├── models.ts # Model loading and routing │ ├── advancedFineTuning.ts # Advanced fine-tuning (LoRA, Adapters, etc.) │ ├── advancedRAG.ts # Advanced RAG with document management │ ├── modelConversion.ts # Model conversion and optimization │ ├── compliance.ts # Compliance monitoring and validation │ ├── advancedAgent.ts # Advanced agents with tools and memory │ ├── modelClientSystem.ts # LSTM, MoE, MultiModal, Federated routing │ ├── gateway.ts # Gateway API and middleware │ ├── contextTransfer.ts # Context transfer functionality │ └── index.ts # Main SDK class and exports ├── example/ │ ├── run-agent.ts # Basic example │ ├── advanced-usage.ts # Advanced example │ ├── comprehensive-demo.ts # Complete feature demo │ └── context-transfer-cli.ts # CLI implementation ├── test/ │ ├── sdk-smoke.test.ts # Basic SDK tests │ ├── module-tests.test.ts # Module functionality tests │ └── cli.test.ts # CLI tests ├── scripts/ │ ├── fix-example-imports.js # Fix example import paths │ ├── fix-all-imports.js # Fix all import paths │ └── sync-features.js # Sync with backend features ├── package.json ├── tsconfig.json └── README.md 

The SDK includes comprehensive error handling. All methods throw errors with descriptive messages when operations fail:

try { const response = await sdk.generateWithAgent("Hello world"); } catch (error) { console.error('Generation failed:', error.message); }

1. Initialization failed: Ensure all required dependencies are installed
2. Model loading issues: Check that model files are accessible and valid
3. Memory issues: For large models, ensure sufficient RAM and consider using quantization
4. GPU issues: Ensure CUDA is properly installed for GPU acceleration
5. Network issues: Check internet connectivity for cloud-based models

Enable debug logging by setting the environment variable:

DEBUG=multimind-sdk npm run dev

# Run all tests npm test # Run tests with coverage npm run test:coverage # Run specific test file npm test -- test/sdk-smoke.test.ts

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

Apache License 2.0 - see LICENSE file for details.

For issues and questions:

• Create an issue on GitHub
• Check the troubleshooting section
• Ensure you have the latest version installed
• Review the comprehensive examples

• MultiMind Documentation
• MultiMind Discord Community

Built with ❤️ by the MultiMind Team