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

const pipe = new NeuroLink();

// Everything is a stream
const result = await pipe.stream({ input: { text: "Hello" } });
for await (const chunk of result.stream) {
  if ("content" in chunk) {
    process.stdout.write(chunk.content);
  }
}

// Multi-Provider Voice (v9.62.0) — TTS + STT
// Voice is configured via the `tts` / `stt` options on generate() / stream(),
// not via dedicated synthesizeSpeech / transcribeAudio methods.

// Text in, audio out (TTS)
const result = await neurolink.generate({
  input: { text: "Hello from NeuroLink" },
  provider: "vertex",
  tts: {
    enabled: true,
    voice: "en-US-Neural2-C",
    format: "mp3",
    output: "./output.mp3", // optional: save to disk
    provider: "elevenlabs", // optional override: openai-tts | elevenlabs | google-ai | vertex | azure-tts | fish-audio | cartesia
  },
});
// result.audio: { buffer: Buffer, format: "mp3", ... }

// Audio in (STT), text out
const transcript = await neurolink.generate({
  input: { text: "Transcribe and summarize" },
  provider: "openai",
  stt: {
    enabled: true,
    audio: audioBuffer, // Buffer of the audio file
    provider: "whisper", // whisper | deepgram | google-stt | azure-stt
    language: "en-US",
  },
});

// Real-time bidirectional voice (OpenAI Realtime / Gemini Live)
import { RealtimeProcessor } from "@juspay/neurolink";

await RealtimeProcessor.connect(
  "openai-realtime",
  { provider: "openai-realtime", model: "gpt-4o-realtime-preview" },
  { onAudio, onTranscript, onError, onFunctionCall },
);

// AutoResearch — autonomous experiment loop (v9.53.0)
import { resolveConfig, ResearchWorker } from "@juspay/neurolink/autoresearch";

const config = resolveConfig({
  repoPath: "/path/to/repo",
  mutablePaths: ["train.py"],
  runCommand: "python3 train.py",
  metric: {
    name: "val_bpb",
    direction: "lower",
    pattern: "^val_bpb:\\s+([\\d.]+)",
  },
});
const worker = new ResearchWorker(config);
await worker.initialize("experiment-1");
const result = await worker.runExperimentCycle("Try lower learning rate");

// Provider Fallback Policy (v9.58.0) — fires only on ModelAccessDeniedError
import { NeuroLink, ModelAccessDeniedError } from "@juspay/neurolink";

const neurolink = new NeuroLink({
  // Async callback. Single error arg. Return null to give up,
  // or { provider?, model? } to retry with a substitute.
  providerFallback: async (error) => {
    if (
      error instanceof ModelAccessDeniedError &&
      error.allowedModels?.length
    ) {
      return { model: error.allowedModels[0] };
    }
    return null;
  },
  // Sugar over providerFallback: if no callback is set, NeuroLink walks this list
  // on each access denial. modelChain is `string[]` only (model names; same provider).
  modelChain: ["claude-opus-4-7", "claude-sonnet-4-6", "gpt-4o"],
});

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

const neurolink = new NeuroLink({
  hitl: {
    enabled: true,
    requireApproval: ["writeFile", "executeCode", "sendEmail"],
    confidenceThreshold: 0.85,
    reviewCallback: async (action, context) => {
      // Custom review logic - integrate with your approval system
      return await yourApprovalSystem.requestReview(action);
    },
  },
});

// AI pauses for human approval before executing sensitive tools
const result = await neurolink.generate({
  input: { text: "Send quarterly report to stakeholders" },
});

# npm
npm install @juspay/neurolink

# pnpm (recommended)
pnpm add @juspay/neurolink

# yarn
yarn add @juspay/neurolink

# CLI only (no installation needed)
npx @juspay/neurolink --help

# Run the setup wizard to configure providers
pnpm dlx @juspay/neurolink setup

# Choose one or more providers
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_AI_API_KEY=...

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

// Initialize (auto-selects best available provider from your .env)
const neurolink = new NeuroLink();

// Generate a response
const result = await neurolink.generate({
  input: { text: "Explain quantum computing in simple terms" },
});

console.log(result.content);

// Stream tokens in real-time
const stream = await neurolink.stream({
  input: { text: "Write a haiku about code" },
});
for await (const chunk of stream.stream) {
  if ("content" in chunk) process.stdout.write(chunk.content);
}

const result = await neurolink.generate({
  input: {
    text: "What's in this image?",
    images: ["./photo.jpg"],
  },
});

// Built-in tools are automatically available
const result = await neurolink.generate({
  input: {
    text: "What time is it and what files are in the current directory?",
  },
  // AI can call getCurrentTime and listDirectory tools
});

# Simple text generation
npx @juspay/neurolink generate "Explain TypeScript generics"

# Specify provider and model
npx @juspay/neurolink generate "Hello!" --provider openai --model gpt-4o

# Stream responses
npx @juspay/neurolink stream "Write a story about AI" --provider anthropic

# Analyze images
npx @juspay/neurolink generate "Describe this image" --image photo.jpg

# Process PDFs
npx @juspay/neurolink generate "Summarize this document" --pdf report.pdf

# Combine multiple file types
npx @juspay/neurolink generate "Analyze this data" --file data.xlsx --file config.json

# Start an interactive session with persistent context
npx @juspay/neurolink loop

# Inside loop mode:
> set provider anthropic
> set model claude-opus-4
> generate "Hello, Claude!"
> history  # View conversation history
> exit

// Automatically chunk, embed, and search documents
const result = await neurolink.generate({
  input: { text: "What are the key features mentioned in the documentation?" },
  rag: {
    files: ["./docs/guide.md", "./docs/api.md"],
    chunkSize: 512,
    topK: 5,
  },
});

import { z } from "zod";

const schema = z.object({
  name: z.string(),
  age: z.number(),
  email: z.string().email(),
});

const result = await neurolink.generate({
  input: {
    text: "Extract user info: John Doe, 30 years old, john@example.com",
  },
  schema,
  output: { format: "json" },
});

// Parse the structured JSON from result.content
const parsed = schema.parse(JSON.parse(result.content));
console.log(parsed); // { name: "John Doe", age: 30, email: "john@example.com" }

// Connect to GitHub MCP server
await neurolink.addExternalMCPServer("github", {
  command: "npx",
  args: ["-y", "@modelcontextprotocol/server-github"],
  transport: "stdio",
  env: { GITHUB_TOKEN: process.env.GITHUB_TOKEN },
});

// AI can now interact with GitHub
const result = await neurolink.generate({
  input: { text: 'Create an issue titled "Bug: login fails"' },
});

// stdio transport - local MCP servers via command execution
await neurolink.addExternalMCPServer("github", {
  command: "npx",
  args: ["-y", "@modelcontextprotocol/server-github"],
  transport: "stdio",
  env: { GITHUB_TOKEN: process.env.GITHUB_TOKEN },
});

// HTTP transport - remote MCP servers via URL
await neurolink.addExternalMCPServer("github-copilot", {
  transport: "http",
  url: "https://api.githubcopilot.com/mcp",
  headers: { Authorization: "Bearer YOUR_COPILOT_TOKEN" },
  timeout: 15000,
  retries: 5,
});

// Tools automatically available to AI
const result = await neurolink.generate({
  input: { text: 'Create a GitHub issue titled "Bug in auth flow"' },
});

import { ToolRouter, ToolCache, RequestBatcher } from "@juspay/neurolink";

// Route tool calls across multiple MCP servers
const router = new ToolRouter({
  strategy: "capability-based",
  servers: [
    { name: "github", url: "https://mcp-github.example.com" },
    { name: "db", url: "https://mcp-postgres.example.com" },
  ],
});

// Cache repeated tool results (LRU, FIFO, or LFU)
const cache = new ToolCache({ strategy: "lru", maxSize: 500, ttl: 60_000 });

// Batch concurrent tool calls for throughput
const batcher = new RequestBatcher({ maxBatchSize: 10, maxWaitMs: 50 });

// Process any supported file type
const result = await neurolink.generate({
  input: {
    text: "Analyze this data and code",
    files: [
      "./data.xlsx", // Excel spreadsheet
      "./config.yaml", // YAML configuration
      "./diagram.svg", // SVG (injected as sanitized text)
      "./main.py", // Python source code
    ],
  },
});

// CLI: Use --file for any supported type
// neurolink generate "Analyze this" --file ./report.xlsx --file ./config.json

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

// Auto-detect Redis from REDIS_URL environment variable
const neurolink = new NeuroLink({
  conversationMemory: {
    enabled: true,
    enableSummarization: true,
  },
});

// Or explicit Redis configuration
const neurolinkExplicit = new NeuroLink({
  conversationMemory: {
    enabled: true,
    redisConfig: {
      host: "redis.example.com",
      port: 6379,
      password: process.env.REDIS_PASSWORD,
      ttl: 86400, // 24-hour session expiration (seconds)
    },
  },
});

// Retrieve conversation history for analytics
const history = await neurolink.getConversationHistory("session-id");
await saveToDataWarehouse(history);

# Start Redis
docker run -d --name neurolink-redis -p 6379:6379 redis:7-alpine

# Configure NeuroLink
export REDIS_URL=redis://localhost:6379

# Start your application
node your-app.js

- uses: juspay/neurolink@v1
  with:
    anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
    prompt: "Review this PR for security issues and code quality"
    post_comment: true

# Cost optimization - automatically use cheapest model
npx @juspay/neurolink generate "Hello" --optimize-cost

# LiteLLM specific model selection
npx @juspay/neurolink generate "Complex analysis" --provider litellm --model "anthropic/claude-sonnet-4-6"

# Auto-select best available provider
npx @juspay/neurolink generate "Write code" # Automatically chooses optimal provider

$ npx @juspay/neurolink loop --enable-conversation-memory

neurolink > /set provider vertex
✓ provider set to vertex (Gemini 3 support enabled)

neurolink > /set model gemini-3-flash-preview
✓ model set to gemini-3-flash-preview

neurolink > Analyze my project architecture and suggest improvements

✓ Analyzing your project structure...
[AI provides detailed analysis, remembering context]

neurolink > Now implement the first suggestion
[AI remembers previous context and implements suggestion]

neurolink > /mcp discover
✓ Discovered 58 MCP tools:
   GitHub: create_issue, list_repos, create_pr...
   PostgreSQL: query, insert, update...
   [full list]

neurolink > Use the GitHub tool to create an issue for this improvement
✓ Creating issue... (requires HITL approval if configured)

neurolink > /export json > session-2026-01-01.json
✓ Exported 15 messages to session-2026-01-01.json

neurolink > exit
Session saved. Resume with: neurolink loop --session session-2026-01-01.json

# Discover available providers and models
npx @juspay/neurolink status
npx @juspay/neurolink models list --provider google-ai

# Route to a specific provider/model
npx @juspay/neurolink generate "Summarize customer feedback" \
  --provider azure --model gpt-4o-mini

# Turn on analytics + evaluation for observability
npx @juspay/neurolink generate "Draft release notes" \
  --enable-analytics --enable-evaluation --format json

# RAG: Ask questions about your docs (auto-chunks, embeds, searches)
npx @juspay/neurolink generate "What are the key features?" \
  --rag-files ./docs/guide.md ./docs/api.md --rag-strategy markdown

# Claude proxy + local OpenObserve dashboard
npx @juspay/neurolink proxy setup
npx @juspay/neurolink proxy telemetry setup
npx @juspay/neurolink proxy status --format json

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

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

const result = await neurolink.generate({
  input: {
    text: "Create a comprehensive analysis",
    files: [
      "./sales_data.csv", // Auto-detected as CSV
      "examples/data/invoice.pdf", // Auto-detected as PDF
      "./diagrams/architecture.png", // Auto-detected as image
      "./report.xlsx", // Auto-detected as Excel
      "./config.json", // Auto-detected as JSON
      "./diagram.svg", // Auto-detected as SVG (injected as text)
      "./app.ts", // Auto-detected as TypeScript code
    ],
  },
  provider: "vertex", // PDF-capable provider (see docs/features/pdf-support.md)
  enableEvaluation: true,
  region: "us-east-1",
});

console.log(result.content);
console.log(result.evaluation?.overallScore);

// RAG: Ask questions about your documents
const answer = await neurolink.generate({
  input: { text: "What are the main architectural decisions?" },
  rag: {
    files: ["./docs/architecture.md", "./docs/decisions.md"],
    strategy: "markdown",
    topK: 5,
  },
});
console.log(answer.content); // AI searches your docs and answers

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

const neurolink = new NeuroLink();

// Use Gemini 3 with extended thinking for complex reasoning
const result = await neurolink.generate({
  input: {
    text: "Solve this step by step: What is the optimal strategy for...",
  },
  provider: "vertex",
  model: "gemini-3-flash-preview",
  thinkingConfig: {
    thinkingLevel: "medium", // Options: "minimal", "low", "medium", "high"
  },
});

console.log(result.content);