API Reference

Complete reference for NeuroLink's TypeScript API.

NeuroLink Class

The NeuroLink class is the main entry point for all SDK functionality.

Constructor: `new NeuroLink(config?)`

Create a new NeuroLink instance with optional configuration for conversation memory, orchestration, HITL, and observability.

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink(config?: NeurolinkConstructorConfig)

Parameters:

type NeurolinkConstructorConfig = {
  // Conversation Memory
  conversationMemory?: Partial<ConversationMemoryConfig>;
  // See ConversationMemoryConfig for full options:
  //   enabled: boolean
  //   maxSessions?: number
  //   enableSummarization?: boolean
  //   tokenThreshold?: number
  //   summarizationProvider?: string
  //   summarizationModel?: string
  //   redisConfig?: RedisStorageConfig
  //   contextCompaction?: { enabled?, threshold?, enablePruning?, ... }
  //   maxTurnsPerSession?: number  // @deprecated - use tokenThreshold instead

  // Provider Orchestration
  enableOrchestration?: boolean;

  // Human-in-the-Loop safety features
  hitl?: HITLConfig;

  // Custom tool registry (advanced)
  toolRegistry?: MCPToolRegistry;

  // Observability (Langfuse integration)
  observability?: ObservabilityConfig;
};

Examples:

import { NeuroLink } from "@juspay/neurolink";

// Basic usage (no configuration)
const neurolink = new NeuroLink();

// With Redis conversation memory
const neurolinkWithMemory = new NeuroLink({
  conversationMemory: {
    enabled: true,
    redisConfig: {
      host: "localhost",
      port: 6379,
      ttl: 7 * 24 * 60 * 60, // 7 days
    },
    tokenThreshold: 100000, // Token threshold to trigger summarization
  },
});

// With HITL safety features
const neurolinkWithHITL = new NeuroLink({
  hitl: {
    enabled: true,
    dangerousActions: ["delete", "remove", "drop"],
    timeout: 30000,
    allowArgumentModification: true,
  },
});

// Complete configuration with all features
const neurolinkComplete = new NeuroLink({
  conversationMemory: {
    enabled: true,
    redisConfig: { host: "localhost", port: 6379 },
    enableSummarization: true,
  },
  enableOrchestration: true,
  hitl: { enabled: true },
  observability: {
    langfuse: {
      enabled: true,
      publicKey: process.env.LANGFUSE_PUBLIC_KEY,
      secretKey: process.env.LANGFUSE_SECRET_KEY,
    },
  },
});

Core Methods

`generate(options)`

Generate text content synchronously.

async generate(options: GenerateOptions): Promise<GenerateResult>

Parameters:

type GenerateOptions = {
  input: {
    text: string;
    images?: Array<string | Buffer>; // Local paths, URLs, or buffers
    csvFiles?: Array<string | Buffer>; // CSV files (converted to text)
    pdfFiles?: Array<string | Buffer>; // PDF files (native binary)
    officeFiles?: Array<string | Buffer>; // Office documents (DOCX, PPTX, XLSX)
    files?: Array<string | Buffer>; // Auto-detect file types
    content?: Array<TextContent | ImageContent>; // Advanced multimodal payloads
  };
  provider?: AIProviderName | string; // Leave undefined to allow orchestration/fallback
  model?: string; // Model slug (e.g., 'gpt-4o', 'veo-3.1')
  region?: string; // Regional routing for providers that support it
  temperature?: number;
  maxTokens?: number;
  systemPrompt?: string;
  schema?: ValidationSchema; // Structured output schema
  tools?: Record<string, Tool>; // Optional tool overrides
  timeout?: number | string; // 120 (seconds) or '2m', '1h'
  disableTools?: boolean;
  maxSteps?: number; // Max tool execution steps (default: 5)
  toolChoice?: ToolChoice; // 'auto' | 'none' | 'required' | { type: 'tool', toolName: string }
  toolChoiceSteps?: number; // Leading steps a forced toolChoice stays in force (default: 1)
  prepareStep?: PrepareStepCallback; // Per-step tool choice — see SDK Custom Tools Guide
  replayToolSteps?: "full" | "marker" | "off"; // How stored tool steps are replayed into the prompt (default: 'marker')
  abortSignal?: AbortSignal; // External cancellation support
  toolFilter?: string[]; // Whitelist of tools to include (only matching tools are available)
  excludeTools?: string[]; // Blacklist of tools to exclude (applied after toolFilter)
  skipToolPromptInjection?: boolean; // Skip injecting tool schemas into system prompt (~30K token savings)
  tts?: TTSOptions; // Text-to-Speech configuration
  maxBudgetUsd?: number; // Per-session USD budget cap — exceeding throws SESSION_BUDGET_EXCEEDED
  workflow?: string; // Use predefined workflow by ID
  workflowConfig?: WorkflowConfig; // Inline workflow configuration
  requestId?: string; // Request ID for observability and log correlation
  enableAnalytics?: boolean;
  enableEvaluation?: boolean;
  evaluationDomain?: string;
  toolUsageContext?: string;
  context?: Record<string, JsonValue>;
  conversationHistory?: Array<{ role: string; content: string }>;
  thinkingLevel?: "minimal" | "low" | "medium" | "high"; // Gemini 3 models only
  thinkingConfig?: {
    // Full thinking/reasoning configuration (takes precedence over thinkingLevel)
    enabled?: boolean;
    type?: "enabled" | "disabled";
    budgetTokens?: number; // Anthropic: 5000-100000 tokens
    thinkingLevel?: "minimal" | "low" | "medium" | "high"; // Gemini 3 models
  };

  // Output configuration
  output?: {
    format?: "text" | "structured" | "json";
    mode?: "text" | "video" | "ppt"; // Output mode: 'text' (default), 'video', or 'ppt'
    video?: VideoOutputOptions; // Video generation options (when mode is 'video')
    ppt?: PPTOutputOptions; // PPT generation options (when mode is 'ppt')
  };

  // Document processing options
  officeOptions?: OfficeProcessorOptions;

  // RAG pipeline configuration
  rag?: RAGConfig; // RAG pipeline config - pass files for automatic chunking and search
};

// Video output configuration (for Veo 3.1 via Vertex AI)
type VideoOutputOptions = {
  resolution?: "720p" | "1080p"; // Video resolution (default: "720p")
  length?: 4 | 6 | 8; // Video duration in seconds (default: 6)
  aspectRatio?: "9:16" | "16:9"; // Aspect ratio (default: "16:9")
  audio?: boolean; // Include synchronized audio (default: true)
};

Returns:

type GenerateResult = {
  content: string;
  provider?: string;
  model?: string;
  usage?: {
    promptTokens: number;
    completionTokens: number;
    totalTokens: number;
  };
  responseTime?: number;
  toolCalls?: Array<{
    toolCallId: string;
    toolName: string;
    args: Record<string, unknown>;
  }>;
  toolResults?: unknown[];
  toolsUsed?: string[];

  // Video generation result (when output.mode is 'video')
  video?: {
    data: Buffer; // Raw video data
    mediaType: string; // MIME type (e.g., 'video/mp4')
    metadata?: {
      duration?: number; // Video duration in seconds
      dimensions?: { width: number; height: number };
      model?: string; // Model used for generation
    };
  };

  // PPT generation result (when output.mode is 'ppt')
  ppt?: {
    filePath: string; // Path to generated PPTX file
    totalSlides: number; // Number of slides generated
    format: "pptx"; // Output format
    provider: string; // Provider used for content planning
    model: string; // Model used for content planning
    metadata?: {
      theme?: string; // Theme applied
      audience?: string; // Target audience
      tone?: string; // Presentation tone
      imageModel?: string; // Model used for image generation
      fileSize?: number; // File size in bytes
    };
  };

  analytics?: {
    provider: string;
    model?: string;
    tokenUsage: { input: number; output: number; total: number };
    cost?: number;
    requestDuration?: number;
    context?: Record<string, JsonValue>;
  };

  evaluation?: {
    relevanceScore: number;
    accuracyScore: number;
    completenessScore: number;
    overallScore: number;
    alertLevel?: "none" | "low" | "medium" | "high";
    reasoning?: string;
    suggestedImprovements?: string;
    domainAlignment?: number;
    terminologyAccuracy?: number;
    toolEffectiveness?: number;
    contextUtilization?: {
      conversationUsed: boolean;
      toolsUsed: boolean;
      domainKnowledgeUsed: boolean;
    };
  };
};

Basic Example:

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

const result = await neurolink.generate({
  input: { text: "Explain quantum computing in simple terms" },
  provider: "openai",
  model: "gpt-4o",
  temperature: 0.7,
  maxTokens: 500,
  systemPrompt: "You are a helpful science teacher",
});

console.log(result.content);
console.log(`Used ${result.usage?.totalTokens} tokens`);
console.log(`Provider: ${result.provider}, Model: ${result.model}`);

With Analytics and Evaluation:

const result = await neurolink.generate({
  input: { text: "Write a business proposal" },
  provider: "openai",
  enableAnalytics: true,
  enableEvaluation: true,
  context: {
    userId: "12345",
    session: "business-meeting",
    department: "sales",
  },
});

// Access enhancement data
console.log("Analytics:", result.analytics);
// { provider: 'openai', model: 'gpt-4o', tokens: {...}, cost: 0.02, responseTime: 2340 }

console.log("Evaluation:", result.evaluation);
// { relevanceScore: 9, accuracyScore: 8, completenessScore: 9, overallScore: 8.7 }

With Video Generation (Veo 3.1):

import { readFile, writeFile } from "fs/promises";

// Generate video from image + text prompt
const result = await neurolink.generate({
  input: {
    text: "Smooth camera movement showcasing the product",
    images: [await readFile("./product-image.jpg")],
  },
  provider: "vertex",
  model: "veo-3.1",
  output: {
    mode: "video",
    video: {
      resolution: "1080p",
      length: 8,
      aspectRatio: "16:9",
      audio: true,
    },
  },
});

// Save generated video
if (result.video) {
  await writeFile("output.mp4", result.video.data);
  console.log(`Video duration: ${result.video.metadata?.duration}s`);
  console.log(
    `Dimensions: ${result.video.metadata?.dimensions?.width}x${result.video.metadata?.dimensions?.height}`,
  );
}

Note: Video generation requires Vertex AI credentials and currently only supports Veo 3.1 model. See Video Generation Guide for complete documentation.

Schema Limitations by Provider

Google Gemini Limitation (Vertex AI and Google AI Studio):

Cannot combine schema + tools (including built-in tools)
Solution: Use disableTools: true when using schemas
Note: This limitation applies to all Gemini models, including Gemini 3 models

Example:

// Will fail with Google providers
const result = await neurolink.generate({
  input: { text: "..." },
  schema: MySchema,
  provider: "vertex", // Error: Function calling with JSON mime type unsupported
});

// Correct for Google providers
const result = await neurolink.generate({
  input: { text: "..." },
  schema: MySchema,
  provider: "vertex",
  disableTools: true, // Required
});

// Works without disableTools
const result = await neurolink.generate({
  input: { text: "..." },
  schema: MySchema,
  provider: "openai", // OpenAI supports both
});

Provider Support Matrix:

Provider	Tools + Schema	Notes
OpenAI	Full Support	No limitations
Anthropic	Full Support	No limitations
Vertex AI (Gemini)	Use `disableTools: true`	Google API limitation
Google AI Studio	Use `disableTools: true`	Google API limitation
Vertex AI (Claude)	Full Support	Uses Anthropic models
Azure OpenAI	Full Support	No limitations
Bedrock	Full Support	No limitations

`stream(options)`

Generate content with streaming responses.

async stream(options: StreamOptions): Promise<StreamResult>

Parameters:

type StreamOptions = {
  input: { text: string };
  output?: {
    format?: "text" | "structured" | "json";
    streaming?: {
      chunkSize?: number;
      bufferSize?: number;
      enableProgress?: boolean;
    };
  };
  provider?: string;
  model?: string;
  temperature?: number;
  maxTokens?: number;
  timeout?: number | string;
  rag?: RAGConfig; // RAG pipeline config - pass files for automatic chunking and search
};

Returns:

type StreamResult = {
  stream: AsyncIterable<
    | { content: string }
    | { type: "audio"; audio: AudioChunk }
    | { type: "image"; imageOutput: { base64: string } }
  >;
  provider?: string;
  model?: string;

  // Usage and completion info
  usage?: TokenUsage; // Token usage statistics (prompt, completion, total)
  finishReason?: string; // Why generation stopped (e.g., "stop", "length", "tool-calls")

  // Tool integration
  toolCalls?: ToolCall[]; // Tool calls made during generation
  toolResults?: ToolResult[]; // Results from tool executions
  toolEvents?: AsyncIterable<ToolExecutionEvent>; // Real-time tool event stream
  toolExecutions?: ToolExecutionSummary[]; // Final summary of all tool executions
  toolsUsed?: string[]; // Names of tools used during generation

  // Stream metadata
  metadata?: {
    streamId?: string;
    startTime?: number;
    totalChunks?: number;
    estimatedDuration?: number;
    responseTime?: number;
    preliminaryTime?: number; // Time to first (preliminary) response
    fallback?: boolean;
    totalToolExecutions?: number;
    toolExecutionTime?: number;
    hasToolErrors?: boolean;
  };

  // Analytics and evaluation (available after stream completion)
  analytics?: AnalyticsData | Promise<AnalyticsData>;
  evaluation?: EvaluationData | Promise<EvaluationData>;

  // Workflow engine integration data
  workflow?: {
    originalResponse: string;
    processedResponse: string;
    ensembleResponses: Array<{
      provider: string;
      model: string;
      content: string;
      responseTime: number;
      status: "success" | "failure" | "timeout" | "partial";
    }>;
    selectedModel: string;
    workflowId: string;
    workflowName: string;
  };
};

Example:

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

const result = await neurolink.stream({
  input: { text: "Write a story about space exploration" },
  provider: "openai",
  temperature: 0.8,
});

for await (const chunk of result.stream) {
  process.stdout.write(chunk.content);
}

`gen(options)`

Short alias for generate(). Identical signature and behavior.

const result = await neurolink.gen({
  input: { text: "Hello" },
  provider: "openai",
});

Embeddings

Generate embeddings directly via the provider's embed() and embedMany() methods.

`provider.embed(text, modelName?)`

Generate an embedding vector for a single text.

import { ProviderFactory } from "@juspay/neurolink";

const provider = await ProviderFactory.createProvider("googleAiStudio");
const embedding = await provider.embed("Hello world");
// embedding: number[] (e.g., 768 dimensions)

`provider.embedMany(texts, modelName?)`

Generate embedding vectors for multiple texts in a single batch. The AI SDK automatically handles chunking for models with batch limits.

const provider = await ProviderFactory.createProvider("openai");
const embeddings = await provider.embedMany([
  "First document",
  "Second document",
  "Third document",
]);
// embeddings: number[][] (e.g., 3 × 1536 dimensions)

Supported providers and default models:

Provider	Default Embedding Model	Env Override
OpenAI	`text-embedding-3-small`	—
Google AI Studio	`gemini-embedding-001`	`GOOGLE_AI_EMBEDDING_MODEL`
Google Vertex	`text-embedding-004`	`VERTEX_EMBEDDING_MODEL`
Amazon Bedrock	`amazon.titan-embed-text-v2:0`	—

RAG Integration

Pass rag: { files: [...] } to generate() or stream() for automatic RAG pipeline setup:

const result = await neurolink.generate({
  prompt: "What does this document say?",
  rag: {
    files: ["./docs/guide.md"],
    strategy: "markdown", // Optional
    topK: 5, // Optional
  },
});

RAGConfig Type:

Property	Type	Default	Description
`files`	`string[]`	required	File paths to load
`strategy`	`ChunkingStrategy`	auto-detected	Chunking strategy
`chunkSize`	`number`	1000	Max chunk size
`chunkOverlap`	`number`	200	Chunk overlap
`topK`	`number`	5	Top results to retrieve
`toolName`	`string`	`"search_knowledge_base"`	Tool name for AI
`toolDescription`	`string`	auto-generated	Tool description
`embeddingProvider`	`string`	generation provider	Embedding provider
`embeddingModel`	`string`	provider default	Embedding model

Exports:

import {
  prepareRAGTool,
  type RAGConfig,
  type RAGPreparedTool,
} from "@juspay/neurolink";

MCP Server Management

`addExternalMCPServer(serverId, config)`

Programmatically add external MCP servers at runtime. Supports stdio, SSE, WebSocket, and HTTP transports.

async addExternalMCPServer(
  serverId: string,
  config: MCPServerInfo
): Promise<ExternalMCPOperationResult<ExternalMCPServerInstance>>

Examples:

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

// Add Bitbucket integration (stdio transport)
await neurolink.addExternalMCPServer("bitbucket", {
  command: "npx",
  args: ["-y", "@nexus2520/bitbucket-mcp-server"],
  env: {
    BITBUCKET_USERNAME: "your-username",
    BITBUCKET_APP_PASSWORD: "your-app-password",
  },
});

// Add HTTP remote server with full configuration
await neurolink.addExternalMCPServer("remote-api", {
  transport: "http",
  url: "https://api.example.com/mcp",
  headers: {
    Authorization: "Bearer YOUR_TOKEN",
    "X-Custom-Header": "value",
  },
  httpOptions: {
    connectionTimeout: 30000,
    requestTimeout: 60000,
    idleTimeout: 120000,
    keepAliveTimeout: 30000,
  },
  retryConfig: {
    maxAttempts: 3,
    initialDelay: 1000,
    maxDelay: 30000,
    backoffMultiplier: 2,
  },
  rateLimiting: {
    requestsPerMinute: 60,
    maxBurst: 10,
    useTokenBucket: true,
  },
});

// Add HTTP server with OAuth 2.1 authentication
await neurolink.addExternalMCPServer("oauth-api", {
  transport: "http",
  url: "https://api.enterprise.com/mcp",
  auth: {
    type: "oauth2",
    oauth: {
      clientId: "your-client-id",
      clientSecret: "your-client-secret",
      authorizationUrl: "https://auth.provider.com/authorize",
      tokenUrl: "https://auth.provider.com/token",
      redirectUrl: "http://localhost:8080/callback",
      scope: "mcp:read mcp:write",
      usePKCE: true,
    },
  },
});

// Add SSE server
await neurolink.addExternalMCPServer("sse-server", {
  transport: "sse",
  url: "https://api.example.com/mcp/sse",
  headers: { Authorization: "Bearer YOUR_TOKEN" },
});

Use Cases:

External service integration (Bitbucket, Slack, Jira)
Custom tool development
Dynamic workflow configuration
Enterprise application toolchain management
Remote MCP server connectivity with authentication
OAuth 2.1 protected enterprise APIs

`getMCPStatus()`

Get current MCP server status and statistics.

async getMCPStatus(): Promise<{
  totalServers: number;
  availableServers: number;
  totalTools: number;
}>

Example:

const status = await neurolink.getMCPStatus();
console.log(`Total servers: ${status.totalServers}`);
console.log(`Available: ${status.availableServers}`);
console.log(`Total tools: ${status.totalTools}`);

Conversation History Management

Currently Available Methods

`getConversationHistory(sessionId)`

Retrieve the complete conversation history for a specific session.

async getConversationHistory(sessionId: string): Promise<ChatMessage[]>

Parameters:

Parameter	Type	Description
`sessionId`	`string`	The session ID to retrieve history for

Returns:

// Array of ChatMessage objects in chronological order
type ChatMessage = {
  role: "user" | "assistant" | "system";
  content: string;
};

Example:

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink({
  conversationMemory: {
    enabled: true,
    redisConfig: { host: "localhost", port: 6379 }, // omit for in-memory
  },
});

// Retrieve conversation history
const history = await neurolink.getConversationHistory("session-abc123");

console.log(`Total messages: ${history.length}`);
for (const message of history) {
  console.log(`[${message.role}]: ${message.content.substring(0, 50)}...`);
}

// Export to JSON file
import { writeFileSync } from "fs";
writeFileSync("conversation.json", JSON.stringify(history, null, 2));

`clearConversationSession(sessionId)`

Clear conversation history for a specific session.

async clearConversationSession(sessionId: string): Promise<boolean>

Parameters:

Parameter	Type	Description
`sessionId`	`string`	The session ID to clear

Returns: boolean - true if session was cleared, false if session didn't exist.

Example:

// Clear a specific session
const cleared = await neurolink.clearConversationSession("session-abc123");
if (cleared) {
  console.log("Session cleared successfully");
} else {
  console.log("Session not found");
}

`clearAllConversations()`

Clear all conversation history across all sessions.

async clearAllConversations(): Promise<void>

Example:

// Clear all conversation history
await neurolink.clearAllConversations();
console.log("All conversations cleared");

Planned Features

Planned Feature

The advanced exportConversationHistory() method with filtering, format options, and metadata is planned for a future release. Currently, use getConversationHistory(sessionId) to retrieve conversation data and process it as needed.

The following advanced export capabilities are planned:

// PLANNED - Not yet available
type ExportOptions = {
  sessionId: string; // Session ID to export
  format?: "json" | "csv"; // Default: 'json'
  includeMetadata?: boolean; // Default: true
  startTime?: Date; // Filter: export from this time
  endTime?: Date; // Filter: export until this time
};

type ConversationHistory = {
  sessionId: string;
  userId?: string;
  createdAt: string;
  updatedAt: string;
  turns: Array<{
    index: number;
    role: "user" | "assistant";
    content: string;
    timestamp: string;
    model?: string;
    provider?: string;
    tokens?: {
      prompt: number;
      completion: number;
    };
  }>;
  metadata?: {
    provider?: string;
    model?: string;
    totalTurns: number;
    toolsUsed?: string[];
  };
};

Planned Feature

The getActiveSessions() method to list all active conversation sessions is planned for a future release.

Workaround: For now, track session IDs in your application when creating conversations:

// Track sessions manually
const activeSessions: string[] = [];

// When starting a conversation
const sessionId = `session-${Date.now()}`;
activeSessions.push(sessionId);

// Retrieve history for all tracked sessions
for (const sessionId of activeSessions) {
  const history = await neurolink.getConversationHistory(sessionId);
  await saveToDatabase(sessionId, history);
}

Using Timeouts

NeuroLink supports flexible timeout configuration for all AI operations:

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

// Numeric milliseconds
const result1 = await neurolink.generate({
  input: { text: "Write a story" },
  provider: "openai",
  timeout: 30000, // 30 seconds
});

// Human-readable formats
const result2 = await neurolink.generate({
  input: { text: "Complex calculation" },
  provider: "openai",
  timeout: "2m", // 2 minutes
});

// Streaming with longer timeout
const stream = await neurolink.stream({
  input: { text: "Generate long content" },
  provider: "openai",
  timeout: "5m", // 5 minutes for streaming
});

Supported Timeout Formats:

Milliseconds: 5000, 30000
Seconds: '30s', '1.5s'
Minutes: '2m', '0.5m'
Hours: '1h', '0.5h'

thinkingLevel Option

The thinkingLevel option controls reasoning depth for Gemini 3 models, enabling more thorough analysis for complex tasks.

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

// Low thinking - fast responses for simple tasks
const quickResult = await neurolink.generate({
  input: { text: "What is 2 + 2?" },
  provider: "google-ai",
  model: "gemini-3-pro",
  thinkingLevel: "low",
});

// Medium thinking - balanced reasoning (default behavior)
const balancedResult = await neurolink.generate({
  input: { text: "Explain the concept of recursion in programming" },
  provider: "google-ai",
  model: "gemini-3-pro",
  thinkingLevel: "medium",
});

// High thinking - deep reasoning for complex problems
const deepResult = await neurolink.generate({
  input: {
    text: "Design a distributed caching system for a high-traffic e-commerce platform",
  },
  provider: "google-ai",
  model: "gemini-3-pro",
  thinkingLevel: "high",
});

console.log(deepResult.content);

thinkingLevel Values:

Level	Description	Use Case
`minimal`	No extended reasoning, fastest	Simple lookups, direct answers
`low`	Minimal reasoning, fast responses	Simple queries, factual lookups
`medium`	Balanced reasoning depth	General tasks, explanations, code generation
`high`	Deep reasoning with extended analysis	Complex problems, architecture design, proofs

Note: The thinkingLevel option is only supported by Gemini 3 models (gemini-3-flash, gemini-3-pro). When used with other providers or models, it will be ignored.

Usage Examples

Basic Text Generation

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

const result = await neurolink.generate({
  input: { text: "Write a haiku about coding" },
  provider: "openai",
  model: "gpt-4o",
});

console.log(result.content);

Multimodal with Images

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

const result = await neurolink.generate({
  input: {
    text: "Describe what you see in this image",
    images: ["path/to/image.jpg"], // Local path or URL
  },
  provider: "openai",
  model: "gpt-4o",
  maxTokens: 500,
});

Office Document Analysis

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

// Analyze Word document
const result = await neurolink.generate({
  input: {
    text: "Summarize this document",
    officeFiles: ["report.docx"],
  },
  provider: "bedrock",
});

// Analyze Excel spreadsheet
const data = await neurolink.generate({
  input: {
    text: "What are the top products by revenue?",
    officeFiles: ["sales-data.xlsx"],
  },
  provider: "bedrock",
});

// Mixed file types with auto-detection
const analysis = await neurolink.generate({
  input: {
    text: "Compare all documents",
    files: ["report.docx", "data.xlsx", "chart.png", "notes.pdf"],
  },
  provider: "bedrock",
});

Provider Fallback with Orchestration

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink({
  enableOrchestration: true, // Enables smart model routing via ModelRouter
});

// Will automatically fallback if primary provider fails
const result = await neurolink.generate({
  input: { text: "Complex reasoning task" },
  // No provider specified - uses orchestration
});

console.log(`Used provider: ${result.provider}`);

Enterprise Configuration Interfaces

`NeuroLinkConfig`

Main configuration interface for enterprise features:

type NeuroLinkConfig = {
  providers: ProviderConfig;
  performance: PerformanceConfig;
  analytics: AnalyticsConfig;
  backup: BackupConfig;
  validation: ValidationConfig;
};

`ExecutionContext`

Rich context interface for all MCP operations:

type ExecutionContext = {
  sessionId?: string;
  userId?: string;
  aiProvider?: string;
  permissions?: string[];
  cacheOptions?: CacheOptions;
  fallbackOptions?: FallbackOptions;
  metadata?: Record<string, unknown>;
  priority?: "low" | "normal" | "high";
  timeout?: number;
  retries?: number;
  correlationId?: string;
  requestId?: string;
  userAgent?: string;
  clientVersion?: string;
  environment?: string;
};

`ToolInfo`

Comprehensive tool metadata interface:

type ToolInfo = {
  name: string;
  description?: string;
  serverId?: string;
  category?: string;
  version?: string;
  parameters?: unknown;
  capabilities?: string[];
  lastUsed?: Date;
  usageCount?: number;
  averageExecutionTime?: number;
};

`ConfigUpdateOptions`

Flexible configuration update options:

type ConfigUpdateOptions = {
  createBackup?: boolean;
  validateBeforeUpdate?: boolean;
  mergeStrategy?: "replace" | "merge" | "deep-merge";
  backupRetention?: number;
  onValidationError?: (errors: ValidationError[]) => void;
  onBackupCreated?: (backupPath: string) => void;
};

`McpRegistry`

Registry interface with optional methods for maximum flexibility:

type McpRegistry = {
  registerServer?(
    serverId: string,
    config?: unknown,
    context?: ExecutionContext,
  ): Promise<void>;
  executeTool?<T>(
    toolName: string,
    args?: unknown,
    context?: ExecutionContext,
  ): Promise<T>;
  listTools?(context?: ExecutionContext): Promise<ToolInfo[]>;
  getStats?(): Record<
    string,
    { count: number; averageTime: number; totalTime: number }
  >;
  unregisterServer?(serverId: string): Promise<void>;
  getServerInfo?(serverId: string): Promise<unknown>;
};

Supported Providers and Models

OpenAI Models

type OpenAIModel =
  | "gpt-4o" // Previous generation multimodal model
  | "gpt-4o-mini" // Default - cost-effective variant
  | "gpt-4-turbo"; // High-performance model

Amazon Bedrock Models

type BedrockModel =
  | "claude-3-7-sonnet" // Default - Latest Claude model
  | "claude-3-5-sonnet" // Previous generation
  | "claude-3-haiku"; // Fast, lightweight model

Note: Bedrock requires full inference profile ARNs in environment variables.

Google Vertex AI Models

type VertexModel =
  | "gemini-2.5-flash" // Default - Fast, efficient
  | "claude-sonnet-4@20250514"; // High-quality reasoning

Google AI Studio Models

type GoogleAIModel =
  | "gemini-2.5-pro" // Default - Latest Gemini Pro
  | "gemini-2.5-flash"; // Fast, efficient responses

Gemini 3 Models (Preview)

Google's latest generation Gemini models with enhanced reasoning capabilities and extended thinking support.

type Gemini3Model =
  | "gemini-3-flash-preview" // Fast, efficient with thinking support (default)
  | "gemini-3-pro-preview"; // Advanced reasoning with maximum thinking depth

Model Variants:

Model	Best For	Thinking Default	Speed
`gemini-3-flash-preview`	Fast tasks, simple queries	`low`	Fastest
`gemini-3-pro-preview`	Complex reasoning, analysis	`high`	Slower

Azure OpenAI Models

type AzureModel = string; // Deployment-specific models
// Common deployments:
// - 'gpt-4o' (default)
// - 'gpt-4-turbo'
// - 'gpt-35-turbo'

Anthropic Models

type AnthropicModel =
  | "claude-3-5-sonnet"
  | "claude-3-opus"
  | "claude-3-sonnet"
  | "claude-3-haiku";

Mistral AI Models

type MistralModel =
  | "mistral-tiny"
  | "mistral-small" // Default
  | "mistral-medium"
  | "mistral-large";

Ollama Models

type OllamaModel = string; // Any locally installed model
// Popular models:
// - 'llama2' (default)
// - 'codellama'
// - 'mistral'
// - 'vicuna'

LiteLLM Models

type LiteLLMModel = string; // Uses provider/model format
// Popular models:
// - 'openai/gpt-4o' (default: openai/gpt-4o-mini)
// - 'anthropic/claude-3-5-sonnet'
// - 'google/gemini-2.0-flash'
// - 'mistral/mistral-large'
// - 'meta/llama-3.1-70b'
// Note: Requires LiteLLM proxy server configuration

Environment Configuration

Required Environment Variables

// OpenAI
OPENAI_API_KEY: string

// Amazon Bedrock
AWS_ACCESS_KEY_ID: string
AWS_SECRET_ACCESS_KEY: string
AWS_REGION?: string              // Default: 'us-east-2'
AWS_SESSION_TOKEN?: string       // For temporary credentials
BEDROCK_MODEL?: string           // Inference profile ARN

// Google Vertex AI (choose one authentication method)
GOOGLE_APPLICATION_CREDENTIALS?: string           // Method 1: File path
GOOGLE_SERVICE_ACCOUNT_KEY?: string              // Method 2: JSON string
GOOGLE_AUTH_CLIENT_EMAIL?: string                // Method 3a: Individual vars
GOOGLE_AUTH_PRIVATE_KEY?: string                 // Method 3b: Individual vars
GOOGLE_VERTEX_PROJECT: string                    // Required for all methods
GOOGLE_VERTEX_LOCATION?: string                  // Default: 'us-east5'

// Google AI Studio
GOOGLE_AI_API_KEY: string                        // API key from AI Studio

// Anthropic
ANTHROPIC_API_KEY?: string                       // Direct Anthropic API

// Azure OpenAI
AZURE_OPENAI_API_KEY?: string                    // Azure OpenAI API key
AZURE_OPENAI_ENDPOINT?: string                   // Azure OpenAI endpoint
AZURE_OPENAI_DEPLOYMENT_ID?: string              // Deployment ID

// Hugging Face
HUGGINGFACE_API_KEY: string                      // HF token from huggingface.co
HUGGINGFACE_MODEL?: string                       // Default: 'Qwen/Qwen2.5-72B-Instruct'

// Ollama (Local)
OLLAMA_BASE_URL?: string                         // Default: 'http://localhost:11434'
OLLAMA_MODEL?: string                            // Default: 'llama2'

// Mistral AI
MISTRAL_API_KEY: string                          // API key from mistral.ai
MISTRAL_MODEL?: string                           // Default: 'mistral-small'

// LiteLLM (100+ Models via Proxy)
LITELLM_BASE_URL?: string                        // Default: 'http://localhost:4000'
LITELLM_API_KEY?: string                         // Default: 'sk-anything'
LITELLM_MODEL?: string                           // Default: 'openai/gpt-4o-mini'

Optional Configuration Variables

// Provider preferences
DEFAULT_PROVIDER?: 'auto' | 'openai' | 'bedrock' | 'vertex' | 'anthropic' | 'azure' | 'google-ai' | 'huggingface' | 'ollama' | 'mistral' | 'litellm'
FALLBACK_PROVIDER?: 'openai' | 'bedrock' | 'vertex' | 'anthropic' | 'azure' | 'google-ai' | 'huggingface' | 'ollama' | 'mistral' | 'litellm'

// Feature toggles
ENABLE_FALLBACK?: 'true' | 'false'

// Debugging
NEUROLINK_DEBUG?: 'true' | 'false'
LOG_LEVEL?: 'error' | 'warn' | 'info' | 'debug'

Type Definitions

Core Types

type ProviderName =
  | "openai"
  | "bedrock"
  | "vertex"
  | "anthropic"
  | "azure"
  | "google-ai"
  | "huggingface"
  | "ollama"
  | "mistral"
  | "litellm";

type GenerateOptions = {
  input: { text: string };
  provider?: ProviderName | string;
  model?: string;
  temperature?: number; // 0.0 to 1.0, default: 0.7
  maxTokens?: number; // Default: 1000
  systemPrompt?: string; // System message
  schema?: any; // For structured output
  timeout?: number | string; // Timeout in ms or human-readable format
  disableTools?: boolean; // Disable tool usage
  enableAnalytics?: boolean; // Enable usage analytics
  enableEvaluation?: boolean; // Enable AI quality scoring
  context?: Record<string, any>; // Custom context for analytics
  thinkingLevel?: "minimal" | "low" | "medium" | "high"; // Gemini 3 models
};

type GenerateResult = {
  content: string;
  provider: string;
  model: string;
  usage?: TokenUsage;
  responseTime?: number; // Milliseconds
  analytics?: {
    provider: string;
    model: string;
    tokens: { input: number; output: number; total: number };
    cost?: number;
    responseTime: number;
    context?: Record<string, any>;
  };
  evaluation?: {
    relevanceScore: number; // 1-10 scale
    accuracyScore: number; // 1-10 scale
    completenessScore: number; // 1-10 scale
    overallScore: number; // 1-10 scale
    alertLevel?: string; // 'none', 'low', 'medium', 'high'
    reasoning?: string; // AI reasoning for the evaluation
  };
};

type TokenUsage = {
  promptTokens: number;
  completionTokens: number;
  totalTokens: number;
};

Office Document Types

Types for processing Office documents (DOCX, PPTX, XLSX):

/**
 * Supported Office document types
 */
type OfficeFileType = "docx" | "pptx" | "xlsx" | "doc" | "xls";

/**
 * Extended file type including Office formats
 */
type FileType = "csv" | "image" | "pdf" | "office" | "text" | "unknown";

/**
 * Office processor options
 */
type OfficeProcessorOptions = {
  /** Provider to use for document processing */
  provider?: string;

  /** Maximum file size in MB (default: 5) */
  maxSizeMB?: number;

  /** Whether to extract embedded images */
  extractImages?: boolean;

  /** Whether to preserve document structure in output */
  preserveStructure?: boolean;
};

/**
 * Office processing result
 */
type OfficeProcessingResult = {
  type: "office";
  content: Buffer;
  mimeType: string;
  metadata: {
    confidence: number;
    size: number;
    filename?: string;
    format: OfficeFileType;
    provider: string;
    estimatedPages?: number;
    hasEmbeddedImages?: boolean;
    hasCharts?: boolean;
  };
};

Office Document Provider Support:

Provider	DOCX	PPTX	XLSX	DOC	XLS	Notes
AWS Bedrock	Yes	Yes	Yes	Yes	Yes	Full native support via Converse API
Google Vertex AI	Yes	Some	Yes	Some	Some	Best for DOCX and XLSX
Anthropic Claude	Yes	Some	Yes	Some	Some	Via document API
OpenAI	No	No	No	No	No	Not supported
Azure OpenAI	No	No	No	No	No	Not supported

Error Handling

Error Types

class AIProviderError extends Error {
  provider: string;
  originalError?: Error;
}

class TimeoutError extends AIProviderError {
  // Thrown when operation exceeds specified timeout
  timeout: number; // Timeout in milliseconds
  operation?: string; // Operation that timed out (e.g., 'generate', 'stream')
}

class ConfigurationError extends AIProviderError {
  // Thrown when provider configuration is invalid
}

class AuthenticationError extends AIProviderError {
  // Thrown when authentication fails
}

class RateLimitError extends AIProviderError {
  // Thrown when rate limits are exceeded
  retryAfter?: number; // Seconds to wait before retrying
}

class QuotaExceededError extends AIProviderError {
  // Thrown when usage quotas are exceeded
}

Error Handling Patterns

import { NeuroLink } from "@juspay/neurolink";
import {
  AIProviderError,
  ConfigurationError,
  AuthenticationError,
  RateLimitError,
  TimeoutError,
} from "@juspay/neurolink";

const neurolink = new NeuroLink();

try {
  const result = await neurolink.generate({
    input: { text: "Hello" },
    provider: "openai",
    timeout: "30s",
  });
} catch (error) {
  if (error instanceof TimeoutError) {
    console.error(`Operation timed out after ${error.timeout}ms`);
    console.error(`Provider: ${error.provider}, Operation: ${error.operation}`);
  } else if (error instanceof ConfigurationError) {
    console.error("Provider not configured:", error.message);
  } else if (error instanceof AuthenticationError) {
    console.error("Authentication failed:", error.message);
  } else if (error instanceof RateLimitError) {
    console.error(`Rate limit exceeded. Retry after ${error.retryAfter}s`);
  } else if (error instanceof AIProviderError) {
    console.error(`Provider ${error.provider} failed:`, error.message);
  } else {
    console.error("Unexpected error:", error);
  }
}

Built-in Tools

Every NeuroLink instance automatically includes these tools:

type BuiltInTools = {
  getCurrentTime: {
    description: "Get the current date and time";
    parameters: { timezone?: string };
  };
  readFile: {
    description: "Read contents of a file";
    parameters: { path: string };
  };
  listDirectory: {
    description: "List contents of a directory";
    parameters: { path: string };
  };
  calculateMath: {
    description: "Perform mathematical calculations";
    parameters: { expression: string };
  };
  writeFile: {
    description: "Write content to a file";
    parameters: { path: string; content: string };
  };
  searchFiles: {
    description: "Search for files by pattern";
    parameters: { pattern: string; path?: string };
  };
};

Example with Tools:

import { NeuroLink } from "@juspay/neurolink";

const neurolink = new NeuroLink();

// Tools are used automatically when appropriate
const result = await neurolink.generate({
  input: { text: "What time is it?" },
  provider: "openai",
});
// Result will use getCurrentTime tool automatically

// Disable tools if needed
const resultNoTools = await neurolink.generate({
  input: { text: "What time is it?" },
  provider: "openai",
  disableTools: true,
});
// Result will use training data instead of real-time tools

Provider Tool Support Status

Provider	Tool Support	Notes
OpenAI	Full	All tools work correctly
Google AI	Full	Excellent tool execution
Anthropic	Full	Reliable tool usage
Azure OpenAI	Full	Same as OpenAI
Mistral	Full	Good tool support
HuggingFace	Partial	Model sees tools but may describe instead of execute
Vertex AI	Partial	Tools available but may not execute
Ollama	Limited	Requires specific models like gemma3n
Bedrock	Full*	Requires valid AWS credentials

Context Compaction

Methods for managing conversation context size within model token limits. Requires conversation memory to be enabled.

`compactSession(sessionId, config?)`

Manually trigger the full 5-stage context compaction pipeline for a session. The pipeline stages are: (0) Relevance drop — needs a decision provider, skipped without one, (1) Tool output pruning, (2) File read deduplication, (3) LLM summarization, (4) Sliding window truncation.

async compactSession(
  sessionId: string,
  config?: CompactionConfig
): Promise<CompactionResult | null>

Parameters:

Parameter	Type	Description
`sessionId`	`string`	The session ID to compact
`config`	`CompactionConfig?`	Optional overrides for summarization provider, model, and behavior

Returns: CompactionResult | null — null if no conversation memory is configured or the session is empty.

type CompactionResult = {
  compacted: boolean; // Whether compaction was performed
  messages: ChatMessage[]; // The compacted messages
  originalTokens?: number; // Token count before compaction
  compactedTokens?: number; // Token count after compaction
};

Example:

const neurolink = new NeuroLink({
  conversationMemory: { enabled: true },
});

const result = await neurolink.compactSession("session-123", {
  provider: "openai",
  summarizationProvider: "openai",
  summarizationModel: "gpt-4o-mini",
});

if (result?.compacted) {
  console.log(
    `Compacted from ${result.originalTokens} to ${result.compactedTokens} tokens`,
  );
}

`getContextStats(sessionId, provider?, model?)`

Get context usage statistics for a session, including token counts and whether compaction is needed.

async getContextStats(
  sessionId: string,
  provider?: string,
  model?: string
): Promise<{
  estimatedInputTokens: number;
  availableInputTokens: number;
  usageRatio: number;
  shouldCompact: boolean;
  messageCount: number;
} | null>

Parameters:

Parameter	Type	Description
`sessionId`	`string`	The session ID to inspect
`provider`	`string?`	Provider name for context window lookup (default: `"openai"`)
`model`	`string?`	Model name for context window lookup

Returns: Stats object or null if conversation memory is not configured or the session is empty.

Example:

const stats = await neurolink.getContextStats(
  "session-123",
  "openai",
  "gpt-4o",
);

if (stats) {
  console.log(
    `Token usage: ${stats.estimatedInputTokens}/${stats.availableInputTokens}`,
  );
  console.log(`Usage ratio: ${(stats.usageRatio * 100).toFixed(1)}%`);
  console.log(`Messages: ${stats.messageCount}`);
  if (stats.shouldCompact) {
    console.log("Context is approaching limit — compaction recommended");
  }
}

`needsCompaction(sessionId, provider?, model?)`

Synchronously check if a session's context exceeds the compaction threshold (80% of the model's context window by default).

needsCompaction(
  sessionId: string,
  provider?: string,
  model?: string
): boolean

Parameters:

Parameter	Type	Description
`sessionId`	`string`	The session ID to check
`provider`	`string?`	Provider name for context window lookup (default: `"openai"`)
`model`	`string?`	Model name for context window lookup

Returns: boolean — true if the session should be compacted, false otherwise (also returns false if memory is not configured or session does not exist).

Example:

if (
  neurolink.needsCompaction("session-123", "anthropic", "claude-3-5-sonnet")
) {
  await neurolink.compactSession("session-123", { provider: "anthropic" });
}

Lifecycle

Methods for gracefully releasing resources held by a NeuroLink instance.

`shutdown()`

Gracefully shut down all NeuroLink resources. Flushes and shuts down OpenTelemetry, closes external MCP server connections, and releases conversation memory resources (e.g., Redis connections).

async shutdown(): Promise<void>

Example:

const neurolink = new NeuroLink();

// ... use neurolink ...

// Graceful shutdown before process exit
await neurolink.shutdown();

`dispose()`

Full resource disposal. Performs everything shutdown() does, plus removes all event listeners, clears circuit breakers, purges internal caches and maps, and resets initialization state. Use this when you are completely done with the instance, especially in test environments where multiple NeuroLink instances are created.

async dispose(): Promise<void>

Example:

const neurolink = new NeuroLink();

try {
  const result = await neurolink.generate({
    input: { text: "Hello" },
    provider: "openai",
  });
} finally {
  // Full cleanup — prevents resource leaks in tests
  await neurolink.dispose();
}

Event System

NeuroLink is a TypedEventEmitter that emits events throughout the generation and streaming lifecycle. Subscribe to events with on(), unsubscribe with off().

const neurolink = new NeuroLink();

neurolink.on("generation:start", (data) => {
  console.log("Generation started");
});

neurolink.on("tool:end", (data) => {
  console.log("Tool execution finished");
});

neurolink.on("stream:chunk", (data) => {
  // Received a streaming chunk
});

Core Events

Event	Emitted When
`generation:start`	A `generate()` call begins
`generation:end`	A `generate()` call completes
`stream:start`	A `stream()` call begins
`stream:chunk`	A new chunk arrives during streaming
`stream:end`	Streaming completes normally
`stream:complete`	Stream fully consumed
`stream:error`	An error occurs during streaming
`tool:start`	A tool execution begins
`tool:end`	A tool execution completes
`response:start`	A provider response begins
`response:end`	A provider response completes

MCP Server Events

Event	Emitted When
`externalMCP:serverConnected`	An external MCP server connects
`externalMCP:serverDisconnected`	An external MCP server disconnects
`externalMCP:serverFailed`	An external MCP server connection fails
`externalMCP:toolDiscovered`	A new tool is discovered on an MCP server
`externalMCP:toolRemoved`	A tool is removed from an MCP server
`externalMCP:serverAdded`	A new MCP server registration is added
`externalMCP:serverRemoved`	An MCP server registration is removed

Other Events

Event	Emitted When
`tools-register:start`	Tool registration process begins
`tools-register:end`	Tool registration process completes
`connected`	Connection established
`message`	General message event
`error`	An error occurs
`log`	A log message is emitted
`log-event`	A structured log event is emitted

TypedEventEmitter Interface:

type TypedEventEmitter<TEvents> = {
  on<K extends keyof TEvents>(
    event: K,
    listener: (...args: unknown[]) => void,
  ): TypedEventEmitter<TEvents>;
  off<K extends keyof TEvents>(
    event: K,
    listener: (...args: unknown[]) => void,
  ): TypedEventEmitter<TEvents>;
  emit<K extends keyof TEvents>(event: K, ...args: unknown[]): boolean;
  removeAllListeners<K extends keyof TEvents>(
    event?: K,
  ): TypedEventEmitter<TEvents>;
  listenerCount<K extends keyof TEvents>(event: K): number;
  listeners<K extends keyof TEvents>(
    event: K,
  ): Array<(...args: unknown[]) => void>;
};

Human-in-the-Loop (HITL) - Mark tools with requiresConfirmation: true
Guardrails Middleware - Enable with middleware: { preset: 'security' }
Conversation History - Use getConversationHistory() method
Multimodal Chat - Use images array in generate() options
Auto Evaluation - Enable with enableEvaluation: true
CLI Loop Sessions - Interactive mode with persistent state
Provider Orchestration - Set enableOrchestration: true
Regional Streaming - Use region parameter in generate()
Office Documents - Use officeFiles array for DOCX, PPTX, XLSX
PDF Support - Use pdfFiles array for PDF documents
CSV Support - Use csvFiles array for spreadsheet data
CLI Commands Reference - CLI equivalents for all SDK methods
Configuration Guide - Environment variables and config files
Troubleshooting - Common SDK issues and solutions

Back to Main README | Next: Visual Demos

NeuroLink Class​

Constructor: new NeuroLink(config?)​

Core Methods​

generate(options)​

Schema Limitations by Provider​

stream(options)​

gen(options)​

Embeddings​

provider.embed(text, modelName?)​

provider.embedMany(texts, modelName?)​

RAG Integration​

MCP Server Management​

addExternalMCPServer(serverId, config)​

getMCPStatus()​

Conversation History Management​

Currently Available Methods​

getConversationHistory(sessionId)​

clearConversationSession(sessionId)​

clearAllConversations()​

Planned Features​

Using Timeouts​

thinkingLevel Option​

Usage Examples​

Basic Text Generation​

Multimodal with Images​

Office Document Analysis​

Provider Fallback with Orchestration​

Enterprise Configuration Interfaces​

NeuroLinkConfig​

ExecutionContext​

ToolInfo​

ConfigUpdateOptions​

McpRegistry​

Supported Providers and Models​

OpenAI Models​

Amazon Bedrock Models​

Google Vertex AI Models​

Google AI Studio Models​

Gemini 3 Models (Preview)​

Azure OpenAI Models​

Anthropic Models​

Mistral AI Models​

Ollama Models​

LiteLLM Models​

Environment Configuration​

Required Environment Variables​

Optional Configuration Variables​

Type Definitions​

Core Types​

Office Document Types​

Error Handling​

Error Types​

Error Handling Patterns​

Built-in Tools​

Provider Tool Support Status​

Context Compaction​

compactSession(sessionId, config?)​

getContextStats(sessionId, provider?, model?)​

needsCompaction(sessionId, provider?, model?)​

Lifecycle​

shutdown()​

dispose()​

Event System​

Core Events​

MCP Server Events​

Other Events​

Related Features​

NeuroLink Class

Constructor: `new NeuroLink(config?)`

Core Methods

`generate(options)`

Schema Limitations by Provider

`stream(options)`

`gen(options)`

Embeddings

`provider.embed(text, modelName?)`

`provider.embedMany(texts, modelName?)`

RAG Integration

MCP Server Management

`addExternalMCPServer(serverId, config)`

`getMCPStatus()`

Conversation History Management

Currently Available Methods

`getConversationHistory(sessionId)`

`clearConversationSession(sessionId)`

`clearAllConversations()`

Planned Features

Using Timeouts

thinkingLevel Option

Usage Examples

Basic Text Generation

Multimodal with Images

Office Document Analysis

Provider Fallback with Orchestration

Enterprise Configuration Interfaces

`NeuroLinkConfig`

`ExecutionContext`

`ToolInfo`

`ConfigUpdateOptions`

`McpRegistry`

Supported Providers and Models

OpenAI Models

Amazon Bedrock Models

Google Vertex AI Models

Google AI Studio Models

Gemini 3 Models (Preview)

Azure OpenAI Models

Anthropic Models

Mistral AI Models

Ollama Models

LiteLLM Models

Environment Configuration

Required Environment Variables

Optional Configuration Variables

Type Definitions

Core Types

Office Document Types

Error Handling

Error Types

Error Handling Patterns

Built-in Tools

Provider Tool Support Status

Context Compaction

`compactSession(sessionId, config?)`

`getContextStats(sessionId, provider?, model?)`

`needsCompaction(sessionId, provider?, model?)`

Lifecycle

`shutdown()`

`dispose()`

Event System

Core Events

MCP Server Events

Other Events

Related Features