About juspay/neurolink
juspay/neurolink is an MCP server published by juspay in the Browser category: universal AI Development Platform with MCP server integration, multi-provider support, and professional CLI. Build, test, and deploy AI applications with multiple ai providers. It has been installed 0 times through Conduid.
The repository has 112 stars and 94 forks, with the last commit 6 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.
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NeuroLink
The pipe layer for the AI nervous system.
AI intelligence flows as streams — tokens, tool calls, memory, voice, documents. NeuroLink is the vascular layer that carries these streams from where they are generated (LLM providers: the neurons) to where they are needed (connectors: the organs).
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);
}
}
→ Docs · → Quick Start · → npm
🧠 What is NeuroLink?
NeuroLink is the universal AI integration platform that unifies 13 major AI providers and 100+ models under one consistent API.
Extracted from production systems at Juspay and battle-tested at enterprise scale, NeuroLink provides a production-ready solution for integrating AI into any application. Whether you're building with OpenAI, Anthropic, Google, AWS Bedrock, Azure, or any of our 13 supported providers, NeuroLink gives you a single, consistent interface that works everywhere.
Why NeuroLink? Switch providers with a single parameter change, leverage 64+ built-in tools and MCP servers, deploy with confidence using enterprise features like Redis memory and multi-provider failover, and optimize costs automatically with intelligent routing. Use it via our professional CLI or TypeScript SDK—whichever fits your workflow.
Where we're headed: We're building for the future of AI—edge-first execution and continuous streaming architectures that make AI practically free and universally available. Read our vision →
What's New (Q1 2026)
| Feature | Version | Description | Guide |
|---|---|---|---|
| Gemini 3 Multi-turn Tool Fix | v9.49.0 | Fixed multi-step agentic tool calling on Vertex AI Gemini 3 models. Correct thoughtSignature replay, stepIndex parallel-call grouping, executionId session isolation, 5-min timeout, silent-timeout surfacing. |
Vertex AI Guide |
| AutoResearch | v9.17.0 | Autonomous AI experiment engine: proposes code changes, runs experiments, evaluates metrics, keeps improvements — unattended for hours. | AutoResearch Guide |
| MCP Enhancements | v9.16.0 | Advanced MCP features: tool routing, result caching, request batching, annotations, elicitation, custom server base, multi-server management | MCP Enhancements Guide |
| Memory | v9.12.0 | Per-user condensed memory that persists across conversations. LLM-powered condensation with S3, Redis, or SQLite backends. | Memory Guide |
| Context Window Management | v9.2.0 | 4-stage compaction pipeline with auto-detection, budget gate at 80% usage, per-provider token estimation | Context Compaction Guide |
| Tool Execution Control | v9.3.0 | prepareStep and toolChoice support for per-step tool enforcement in multi-step agentic loops. API-level control over tool calls. |
API Reference |
| File Processor System | v9.1.0 | 17+ file type processors with ProcessorRegistry, security sanitization, SVG text injection | File Processors Guide |
| RAG with generate()/stream() | v9.2.0 | Pass rag: { files } to generate/stream for automatic document chunking, embedding, and AI-powered search. 10 chunking strategies, hybrid search, reranking. |
RAG Guide |
| External TracerProvider Support | v8.43.0 | Integrate NeuroLink with existing OpenTelemetry instrumentation. Prevents duplicate registration conflicts. | Observability Guide |
| Server Adapters | v8.43.0 | Multi-framework HTTP server with Hono, Express, Fastify, Koa support. Full CLI for server management with foreground/background modes. | Server Adapters Guide |
| Title Generation Events | v8.38.0 | Emit conversation:titleGenerated event when conversation title is generated. Supports custom title prompts via NEUROLINK_TITLE_PROMPT. |
Conversation Memory Guide |
| Video Generation with Veo | v8.32.0 | Video generation using Veo 3.1 (veo-3.1). Realistic video generation with many parameter options |
Video Generation Guide |
| Image Generation with Gemini | v8.31.0 | Native image generation using Gemini 2.0 Flash Experimental (imagen-3.0-generate-002). High-quality image synthesis directly from Google AI. |
Image Generation Guide |
| HTTP/Streamable HTTP Transport | v8.29.0 | Connect to remote MCP servers via HTTP with authentication headers, automatic retry with exponential backoff, and configurable rate limiting. | HTTP Transport Guide |
- AutoResearch – Autonomous AI experiment engine inspired by Karpathy's autoresearch. Phase-gated tool access, git-backed safety, deterministic metric evaluation, and TaskManager integration for continuous unattended research. 12 research tools, 10 typed events, 9 CLI subcommands. → AutoResearch Guide
- Memory – Per-user condensed memory that persists across all conversations. Automatically retrieves and stores memory on each
generate()/stream()call. Supports S3, Redis, and SQLite storage with LLM-powered condensation. → Memory Guide - External TracerProvider Support – Integrate NeuroLink with applications that already have OpenTelemetry instrumentation. Supports auto-detection and manual configuration. → Observability Guide
- Claude Proxy Telemetry – Bootstrap a local OpenObserve + OTEL collector stack with
neurolink proxy telemetry setup, import the maintained NeuroLink Proxy Observability dashboard, and inspect proxy logs, traces, metrics, cache reuse, and routing behavior. → Claude Proxy Guide | Proxy Observability Guide - Server Adapters – Deploy NeuroLink as an HTTP API server with your framework of choice (Hono, Express, Fastify, Koa). Full CLI support with
serveandservercommands for foreground/background modes, route management, and OpenAPI generation. → Server Adapters Guide - Title Generation Events – Emit real-time events when conversation titles are auto-generated. Listen to
conversation:titleGeneratedfor session tracking. → Conversation Memory Guide - Custom Title Prompts – Customize conversation title generation with
NEUROLINK_TITLE_PROMPTenvironment variable. Use${userMessage}placeholder for dynamic prompts. → Conversation Memory Guide - Video Generation – Transform images into 8-second videos with synchronized audio using Google Veo 3.1 via Vertex AI. Supports 720p/1080p resolutions, portrait/landscape aspect ratios. → Video Generation Guide
- PPT Generation – Create professional PowerPoint presentations from text prompts with 35 slide types (title, content, charts, timelines, dashboards, composite layouts), 5 themes, and optional AI-generated images. Works with Vertex AI, OpenAI, Anthropic, Google AI, Azure, and Bedrock. → PPT Generation Guide
- Image Generation – Generate images from text prompts using Gemini models via Vertex AI or Google AI Studio. Supports streaming mode with automatic file saving. → Image Generation Guide
- RAG with generate()/stream() – Just pass
rag: { files: ["./docs/guide.md"] }togenerate()orstream(). NeuroLink auto-chunks, embeds, and creates a search tool the AI can invoke. 10 chunking strategies, hybrid search, 5 reranker types. → RAG Guide - HTTP/Streamable HTTP Transport for MCP – Connect to remote MCP servers via HTTP with authentication headers, retry logic, and rate limiting. → HTTP Transport Guide
- 🧠 Gemini 3 Native Multi-turn Tool Calling — Fixed multi-step agentic tool calling for Gemini 3 models on Vertex AI. The native
@google/genaipath now correctly replaysthoughtSignatureas a sibling field on eachfunctionCallpart, groups parallel tool calls bystepIndex, enforces a 5-minute default timeout on the generate path, and surfaces silent timeouts as properTimeoutErrorinstead of empty responses. Multi-execution session overlap (wherecontinueOrchestratorWorkflowrestarts the loop on the samesessionId) is addressed by anexecutionIdper invocation as a composite grouping key — this prevents tool calls from two different executions colliding into the same Gemini model turn and causing the model to return 0 function calls. - 🧠 Gemini 3 Preview Support - Full support for gemini-3-flash-preview and gemini-3-pro-preview with extended thinking capabilities
- 🎯 Tool Execution Control – Use
prepareStepto enforce specific tool calls, change the LLM models per step in multi-step agentic executions. Prevents LLMs from skipping required tools. UsetoolChoicefor static control, orprepareStepfor dynamic per-step logic. → GenerateOptions Reference - Structured Output with Zod Schemas – Type-safe JSON generation with automatic validation using
schema+output.format: "json"ingenerate(). → Structured Output Guide - CSV File Support – Attach CSV files to prompts for AI-powered data analysis with auto-detection. → CSV Guide
- PDF File Support – Process PDF documents with native visual analysis for Vertex AI, Anthropic, Bedrock, AI Studio. → PDF Guide
- 50+ File Types – Process Excel, Word, RTF, JSON, YAML, XML, HTML, SVG, Markdown, and 50+ code languages with intelligent content extraction. → File Processors Guide
- LiteLLM Integration – Access 100+ AI models from all major providers through unified interface. → Setup Guide
- SageMaker Integration – Deploy and use custom trained models on AWS infrastructure. → Setup Guide
- OpenRouter Integration – Access 300+ models from OpenAI, Anthropic, Google, Meta, and more through a single unified API. → Setup Guide
- Human-in-the-loop workflows – Pause generation for user approval/input before tool execution. → HITL Guide
- Guardrails middleware – Block PII, profanity, and unsafe content with built-in filtering. → Guardrails Guide
- Context summarization – Automatic conversation compression for long-running sessions. → Summarization Guide
- MCP Enhancements – 14 production-grade modules: tool routing (6 strategies), result caching (LRU/FIFO/LFU), request batching, tool annotations with auto-inference, middleware chain, elicitation protocol, multi-server management, and more. → MCP Enhancements Guide
- Redis conversation export – Export full session history as JSON for analytics and debugging. → History Guide
// Image Generation with Gemini (v8.31.0)
const image = await neurolink.generate({
input: { text: "A futuristic cityscape" },
provider: "google-ai",
model: "imagen-3.0-generate-002",
});
console.log(image.imageOutput?.base64); // Base64-encoded image
// AutoResearch — autonomous experiment loop (v9.17.0)
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");
// HTTP Transport for Remote MCP (v8.29.0)
await neurolink.addExternalMCPServer("remote-tools", {
transport: "http",
url: "https://mcp.example.com/v1",
headers: { Authorization: "Bearer token" },
retries: 3,
timeout: 15000,
});
- Image Generation – Generate images from text prompts using Gemini models via Vertex AI or Google AI Studio. → Guide
- Gemini 3 Preview Support - Full support for
gemini-3-flash-previewandgemini-3-pro-previewwith extended thinking - Structured Output with Zod Schemas – Type-safe JSON generation with automatic validation. → Guide
- CSV & PDF File Support – Attach CSV/PDF files to prompts with auto-detection. → CSV | PDF
- LiteLLM & SageMaker – Access 100+ models via LiteLLM, deploy custom models on SageMaker. → LiteLLM | SageMaker
- OpenRouter Integration – Access 300+ models through a single unified API. → Guide
- HITL & Guardrails – Human-in-the-loop approval workflows and content filtering middleware. → HITL | Guardrails
- Redis & Context Management – Session export, conversation history, and automatic summarization. → History
Enterprise Security: Human-in-the-Loop (HITL)
NeuroLink includes a production-ready HITL system for regulated industries and high-stakes AI operations:
| Capability | Description | Use Case |
|---|---|---|
| Tool Approval Workflows | Require human approval before AI executes sensitive tools | Financial transactions, data modifications |
| Output Validation | Route AI outputs through human review pipelines | Medical diagnosis, legal documents |
| Confidence Thresholds | Automatically trigger human review below confidence level | Critical business decisions |
| Complete Audit Trail | Full audit logging for compliance (HIPAA, SOC2, GDPR) | Regulated industries |
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" },
});
Enterprise HITL Guide | Quick Start
📚 Quick Start Guide
This guide will have you generating AI responses in under 5 minutes using either the SDK or CLI.
Installation
Choose your preferred package manager:
# 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
Configuration
NeuroLink works with 13+ AI providers. You'll need at least one API key to get started:
Option 1: Interactive Setup (Recommended)
# Run the setup wizard to configure providers
pnpm dlx @juspay/neurolink setup
The wizard will guide you through:
- Selecting your preferred AI providers
- Validating API keys
- Setting up configuration files
Option 2: Manual Configuration
Create a .env file in your project root:
# Choose one or more providers
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_AI_API_KEY=...
Free Tier Options:
- Google AI Studio: Get a free API key at aistudio.google.com
- Mistral AI: Free tier available at console.mistral.ai
- Ollama: 100% free local models (requires Ollama installation)
Your First API Call (SDK)
Basic Text Generation:
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);
Streaming Responses:
// 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);
}
Multimodal Input (Images + Text):
const result = await neurolink.generate({
input: {
text: "What's in this image?",
images: ["./photo.jpg"],
},
});
Using Tools:
// 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
});
Your First API Call (CLI)
Basic Generation:
# 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
Multimodal Input:
# 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
Interactive Loop Mode:
# 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
Common Use Cases
RAG (Retrieval-Augmented Generation):
// 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,
},
});
Structured Output with Zod:
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" }
External MCP Servers (GitHub, Slack, etc.):
// 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"' },
});
Next Steps
- Complete Documentation - Comprehensive guides and API reference
- Provider Setup Guide - Configure all 13 providers
- SDK API Reference - Full TypeScript API documentation
- CLI Command Reference - Complete CLI documentation
- Example Projects - Real-world integration examples
- Advanced Features - Middleware, observability, workflows
Troubleshooting
Issue: "Provider not configured"
- Run
npx @juspay/neurolink setupor add provider API key to.env
Issue: Rate limit errors
- Configure multiple providers for redundancy — NeuroLink auto-selects the best available
- Use
provider: "litellm"with LiteLLM to proxy across many providers
Issue: Large context overflows
- Enable conversation memory with compaction:
new NeuroLink({ conversationMemory: { enabled: true } }) - Use
ragoption to search documents instead of sending full content
Need help? Check our Troubleshooting Guide or open an issue.
🌟 Complete Feature Set
NeuroLink is a comprehensive AI development platform. Every feature below is production-ready and fully documented.
🤖 AI Provider Integration
13 providers unified under one API - Switch providers with a single parameter change.
| Provider | Models | Free Tier | Tool Support | Status | Documentation |
|---|---|---|---|---|---|
| OpenAI | GPT-4o, GPT-4o-mini, o1 | ❌ | ✅ Full | ✅ Production | Setup Guide |
| Anthropic | Claude 4.5 Opus/Sonnet/Haiku, Claude 4 Opus/Sonnet | ❌ | ✅ Full | ✅ Production | Setup Guide | Subscription Guide |
| Google AI Studio | Gemini 3 Flash/Pro, Gemini 2.5 Flash/Pro | ✅ Free Tier | ✅ Full | ✅ Production | Setup Guide |
| AWS Bedrock | Claude, Titan, Llama, Nova | ❌ | ✅ Full | ✅ Production | Setup Guide |
| Google Vertex | Gemini 3/2.5 (gemini-3-*-preview) | ❌ | ✅ Full | ✅ Production | Setup Guide |
| Azure OpenAI | GPT-4, GPT-4o, o1 | ❌ | ✅ Full | ✅ Production | Setup Guide |
| LiteLLM | 100+ models unified | Varies | ✅ Full | ✅ Production | Setup Guide |
| AWS SageMaker | Custom deployed models | ❌ | ✅ Full | ✅ Production | Setup Guide |
| Mistral AI | Mistral Large, Small | ✅ Free Tier | ✅ Full | ✅ Production | Setup Guide |
| Hugging Face | 100,000+ models | ✅ Free | ⚠️ Partial | ✅ Production | Setup Guide |
| Ollama | Local models (Llama, Mistral) | ✅ Free (Local) | ⚠️ Partial | ✅ Production | Setup Guide |
| OpenAI Compatible | Any OpenAI-compatible endpoint | Varies | ✅ Full | ✅ Production | Setup Guide |
| OpenRouter | 200+ Models via OpenRouter | Varies | ✅ Full | ✅ Production | Setup Guide |
📖 Provider Comparison Guide - Detailed feature matrix and selection criteria 🔬 Provider Feature Compatibility - Test-based compatibility reference for all 19 features across 13 providers
🔧 Built-in Tools & MCP Integration
6 Core Tools (work across all providers, zero configuration):
| Tool | Purpose | Auto-Available | Documentation |
|---|---|---|---|
getCurrentTime |
Real-time clock access | ✅ | Tool Reference |
readFile |
File system reading | ✅ | Tool Reference |
writeFile |
File system writing | ✅ | Tool Reference |
listDirectory |
Directory listing | ✅ | Tool Reference |
calculateMath |
Mathematical operations | ✅ | Tool Reference |
websearchGrounding |
Google Vertex web search | ⚠️ Requires credentials | Tool Reference |
58+ External MCP Servers supported (GitHub, PostgreSQL, Google Drive, Slack, and more):
// 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"' },
});
MCP Transport Options:
| Transport | Use Case | Key Features |
|---|---|---|
stdio |
Local servers | Command execution, environment variables |
http |
Remote servers | URL-based, auth headers, retries, rate limiting |
sse |
Event streams | Server-Sent Events, real-time updates |
websocket |
Bi-directional | Full-duplex communication |
📖 MCP Integration Guide - Setup external servers 📖 HTTP Transport Guide - Remote MCP server configuration
🔌 MCP Enhancements
Production-grade MCP capabilities for managing tool calls at scale across multi-server environments:
| Module | Purpose |
|---|---|
| Tool Router | Intelligent routing across servers with 6 strategies |
| Tool Cache | Result caching with LRU, FIFO, and LFU eviction |
| Request Batcher | Automatic batching of tool calls for throughput |
| Tool Annotations | Safety metadata and behavior hints for MCP tools |
| Tool Converter | Bidirectional conversion between NeuroLink and MCP formats |
| Elicitation Protocol | Interactive user input during tool execution (HITL) |
| Multi-Server Manager | Load balancing and failover across server groups |
| MCP Server Base | Abstract base class for building custom MCP servers |
| Enhanced Tool Discovery | Advanced search and filtering across servers |
| Agent & Workflow Exposure | Expose agents and workflows as MCP tools |
| Server Capabilities | Resource and prompt management per MCP spec |
| Registry Client | Discover and connect to MCP servers from registries |
| Tool Integration | End-to-end tool lifecycle with middleware chain |
| Elicitation Manager | Manages elicitation flows with validation and timeouts |
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 });
📖 MCP Enhancements Guide - Full reference for all 14 modules
💻 Developer Experience Features
SDK-First Design with TypeScript, IntelliSense, and type safety:
| Feature | Description | Documentation |
|---|---|---|
| Auto Provider Selection | Intelligent provider fallback | SDK Guide |
| Streaming Responses | Real-time token streaming | Streaming Guide |
| Conversation Memory | Automatic context management with embedded per-user memory | Memory Guide |
| Full Type Safety | Complete TypeScript types | Type Reference |
| Error Handling | Graceful provider fallback | Error Guide |
| Analytics & Evaluation | Usage tracking, quality scores | Analytics Guide |
| Middleware System | Request/response hooks | Middleware Guide |
| Framework Integration | Next.js, SvelteKit, Express | Framework Guides |
| Extended Thinking | Native thinking/reasoning mode for Gemini 3 and Claude models | Thinking Guide |
| RAG Document Processing | rag: { files } on generate/stream with 10 chunking strategies and hybrid search |
RAG Guide |
📁 Multimodal & File Processing
17+ file categories supported (50+ total file types including code languages) with intelligent content extraction and provider-agnostic processing:
| Category | Supported Types | Processing |
|---|---|---|
| Documents | Excel (.xlsx, .xls), Word (.docx), RTF, OpenDocument |
Sheet extraction, text extraction |
| Data | JSON, YAML, XML | Validation, syntax highlighting |
| Markup | HTML, SVG, Markdown, Text | OWASP-compliant sanitization |
| Code | 50+ languages (TypeScript, Python, Java, Go, etc.) | Language detection, syntax metadata |
| Config | .env, .ini, .toml, .cfg |
Secure parsing |
| Media | Images (PNG, JPEG, WebP, GIF), PDFs, CSV | Provider-specific formatting |
// 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
Key Features:
- ProcessorRegistry - Priority-based processor selection with fallback
- OWASP Security - HTML/SVG sanitization prevents XSS attacks
- Auto-detection - FileDetector identifies file types by extension and content
- Provider-agnostic - All processors work across all 13 AI providers
📖 File Processors Guide - Complete reference for all file types
🏢 Enterprise & Production Features
Production-ready capabilities for regulated industries:
| Feature | Description | Use Case | Documentation |
|---|---|---|---|
| Enterprise Proxy | Corporate proxy support | Behind firewalls | Proxy Setup |
| Redis Memory | Distributed conversation state | Multi-instance deployment | Redis Guide |
| Memory | Per-user condensed memory (S3/Redis/SQLite) | Long-term user context | Memory Guide |
| Cost Optimization | Automatic cheapest model selection | Budget control | Cost Guide |
| Multi-Provider Failover | Automatic provider switching | High availability | Failover Guide |
| Telemetry & Monitoring | OpenTelemetry integration | Observability | Telemetry Guide |
| Security Hardening | Credential management, auditing | Compliance | Security Guide |
| Custom Model Hosting | SageMaker integration | Private models | SageMaker Guide |
| Load Balancing | LiteLLM proxy integration | Scale & routing | Load Balancing |
Security & Compliance:
- ✅ SOC2 Type II compliant deployments
- ✅ ISO 27001 certified infrastructure compatible
- ✅ GDPR-compliant data handling (EU providers available)
- ✅ HIPAA compatible (with proper configuration)
- ✅ Hardened OS verified (SELinux, AppArmor)
- ✅ Zero credential logging
- ✅ Encrypted configuration storage
- ✅ Automatic context window management with 4-stage compaction pipeline and 80% budget gate
📖 Enterprise Deployment Guide - Complete production checklist
Enterprise Persistence: Redis Memory
Production-ready distributed conversation state for multi-instance deployments:
Capabilities
| Feature | Description | Benefit |
|---|---|---|
| Distributed Memory | Share conversation context across instances | Horizontal scaling |
| Session Export | Export full history as JSON | Analytics, debugging, audit |
| Auto-Detection | Automatic Redis discovery from environment | Zero-config in containers |
| Graceful Failover | Falls back to in-memory if Redis unavailable | High availability |
| TTL Management | Configurable session expiration | Memory management |
Quick Setup
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);
Docker Quick Start
# 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
Redis Setup Guide | Production Configuration | Migration Patterns
🎨 Professional CLI
15+ commands for every workflow:
| Command | Purpose | Example | Documentation |
|---|---|---|---|
setup |
Interactive provider configuration | neurolink setup |
Setup Guide |
generate |
Text generation | neurolink gen "Hello" |
Generate |
stream |
Streaming generation | neurolink stream "Story" |
Stream |
status |
Provider health check | neurolink status |
Status |
loop |
Interactive session | neurolink loop |
Loop |
mcp |
MCP server management | neurolink mcp discover |
MCP CLI |
models |
Model listing | neurolink models |
Models |
eval |
Model evaluation | neurolink eval |
Eval |
serve |
Start HTTP server in foreground mode | neurolink serve |
Serve |
server start |
Start HTTP server in background mode | neurolink server start |
Server |
server stop |
Stop running background server | neurolink server stop |
Server |
server status |
Show server status information | neurolink server status |
Server |
server routes |
List all registered API routes | neurolink server routes |
Server |
server config |
View or modify server configuration | neurolink server config |
Server |
server openapi |
Generate OpenAPI specification | neurolink server openapi |
Server |
rag chunk |
Chunk documents for RAG | neurolink rag chunk f.md |
RAG CLI |
RAG flags are available on generate and stream: --rag-files, --rag-strategy, --rag-chunk-size, --rag-chunk-overlap, --rag-top-k
📖 Complete CLI Reference - All commands and options
🤖 GitHub Action
Run AI-powered workflows directly in GitHub Actions with 13 provider support and automatic PR/issue commenting.
- 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
| Feature | Description |
|---|---|
| Multi-Provider | 13 providers with unified interface |
| PR/Issue Comments | Auto-post AI responses with intelligent updates |
| Multimodal Support | Attach images, PDFs, CSVs, Excel, Word, JSON, YAML, XML, HTML, SVG, code files to prompts |
| Cost Tracking | Built-in analytics and quality evaluation |
| Extended Thinking | Deep reasoning with thinking tokens |
📖 GitHub Action Guide - Complete setup and examples
💰 Smart Model Selection
NeuroLink features intelligent model selection and cost optimization:
Cost Optimization Features
- 💰 Automatic Cost Optimization: Selects cheapest models for simple tasks
- 🔄 LiteLLM Model Routing: Access 100+ models with automatic load balancing
- 🔍 Capability-Based Selection: Find models with specific features (vision, function calling)
- ⚡ Intelligent Fallback: Seamless switching when providers fail
# 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-3-5-sonnet"
# Auto-select best available provider
npx @juspay/neurolink generate "Write code" # Automatically chooses optimal provider
Revolutionary Interactive CLI
NeuroLink's CLI goes beyond simple commands - it's a full AI development environment:
Why Interactive Mode Changes Everything
| Feature | Traditional CLI | NeuroLink Interactive |
|---|---|---|
| Session State | None | Full persistence |
| Memory | Per-command | Conversation-aware |
| Configuration | Flags per command | /set persists across session |
| Tool Testing | Manual per tool | Live discovery & testing |
| Streaming | Optional | Real-time default |
Live Demo: Development Session
$ 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
Session Commands Reference
| Command | Purpose |
|---|---|
/set <key> <value> |
Persist configuration (provider, model, temperature) |
/mcp discover |
List all available MCP tools |
/export json |
Export conversation to JSON |
/history |
View conversation history |
/clear |
Clear context while keeping settings |
Interactive CLI Guide | CLI Reference
Skip the wizard and configure manually? See docs/getting-started/provider-setup.md.
CLI & SDK Essentials
neurolink CLI mirrors the SDK so teams can script experiments and codify them later.
# 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
Gemini 3 with Extended Thinking
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);
Full command and API breakdown lives in docs/cli/commands.md and docs/sdk/api-reference.md.
Platform Capabilities at a Glance
| Capability | Highlights |
|---|---|
| Provider unification | 13+ providers with automatic fallback, cost-aware routing, provider orchestration (Q3). |
| Multimodal pipeline | Stream images + CSV data + PDF documents across providers with local/remote assets. Auto-detection for mixed file types. |
| Quality & governance | Auto-evaluation engine (Q3), guardrails middleware (Q4), HITL workflows (Q4), audit logging. |
| Memory & context | Conversation memory, Redis history export (Q4), context summarization (Q4). |
| CLI tooling | Loop sessions (Q3), setup wizard, config validation, Redis auto-detect, JSON output. |
| Enterprise ops | Proxy support, regional routing (Q3), telemetry hooks, local OpenObserve dashboard setup, configuration management. |
| Tool ecosystem | MCP auto discovery, HTTP/stdio/SSE/WebSocket transports, LiteLLM hub access, SageMaker custom deployment, web search. |
Documentation Map
| Area | When to Use | Link |
|---|---|---|
| Getting started | Install, configure, run first prompt | docs/getting-started/index.md |
| Feature guides | Understand new functionality front-to-back | docs/features/index.md |
| CLI reference | Command syntax, flags, loop sessions | docs/cli/index.md |
| SDK reference | Classes, methods, options | docs/sdk/index.md |
| RAG | Document chunking, hybrid search, reranking, rag:{} API |
docs/features/rag.md |
| Integrations | LiteLLM, SageMaker, MCP | docs/litellm-integration.md |
| Advanced | Middleware, architecture, streaming patterns | docs/advanced/index.md |
| Cookbook | Practical recipes for common patterns | docs/cookbook/index.md |
| Guides | Migration, Redis, troubleshooting, provider selection | docs/guides/index.md |
| Operations | Configuration, troubleshooting, provider matrix | docs/reference/index.md |
New in 2026: Enhanced Documentation
Enterprise Features:
- Enterprise HITL Guide - Production-ready approval workflows
- Interactive CLI Guide - AI development environment
- MCP Tools Showcase - 58+ external tools & 6 built-in tools
Provider Intelligence:
- Provider Capabilities Audit - Technical capabilities matrix
- Provider Selection Guide - Interactive decision wizard
- Provider Comparison - Feature & cost comparison
Middleware System:
- Middleware Architecture - Complete lifecycle & patterns
- Built-in Middleware - Analytics, Guardrails, Evaluation
- Custom Middleware Guide - Build your own
Redis & Persistence:
- Redis Quick Start - 5-minute setup
- Redis Configuration - Production-ready setup
- Redis Migration - Migration patterns
Migration Guides:
- From LangChain - Complete migration guide
- From Vercel AI SDK - Next.js focused
Developer Experience:
- Cookbook - 10 practical recipes
- Troubleshooting Guide - Common issues & solutions
Integrations
- LiteLLM 100+ model hub – Unified access to third-party models via LiteLLM routing. →
docs/litellm-integration.md - Amazon SageMaker – Deploy and call custom endpoints directly from NeuroLink CLI/SDK. →
docs/sagemaker-integration.md - Enterprise proxy & security – Configure outbound policies and compliance posture. →
docs/enterprise-proxy-setup.md - Configuration automation – Manage environments, regions, and credentials safely. →
docs/configuration-management.md - MCP tool ecosystem – Auto-discover Model Context Protocol tools and extend workflows. →
docs/advanced/mcp-integration.md - Remote MCP via HTTP – Connect to HTTP-based MCP servers with authentication, retries, and rate limiting. →
docs/mcp-http-transport.md
Contributing & Support
- Bug reports and feature requests → GitHub Issues
- Development workflow, testing, and pull request guidelines →
docs/development/contributing.md - Documentation improvements → open a PR referencing the documentation matrix.
NeuroLink is built with ❤️ by Juspay. Contributions, questions, and production feedback are always welcome.
README mirrored from the source repository 3 hours ago. The original is authoritative.