Compare NeuroLink's five reranking strategies for RAG — simple scoring, LLM-based, batch, cross-encoder, and Cohere — with benchmarks, cost analysis, and guidance on when to use each.

NeuroLink v9.30 lifecycle middleware hooks let you tap into onFinish, onError, and onChunk events for logging, analytics, error recovery, and real-time monitoring without touching business logic.

NeuroLink ships 10 chunking strategies for RAG pipelines — from simple character splitting to semantic chunking. This guide benchmarks all 10 and shows when to use each for optimal retrieval quality.

How Juspay built Yama, an AI-native code review tool using NeuroLink SDK with Bitbucket and Jira MCP connectors to review PRs across security, runtime, performance, and code quality focus areas.

NeuroLink's native Gemini 3 and 3.1 integration using Google's SDK directly for thought signatures, streaming tool calls, and multimodal input across both AI Studio and Vertex AI.

How NeuroLink automatically infers tool annotations like idempotency, retry safety, and timeout requirements from tool definitions, enabling intelligent routing, caching, and error recovery without manual configuration.

How NeuroLink borrows from neuroscience to build an AI nervous system — neurons as LLM providers, pipes for token routing with built-in RAG, memory, and file processing, and organs as the applications that consume intelligence.

How NeuroLink's MCP auto-discovery dynamically finds and registers tools from any MCP server at runtime, plus the elicitation protocol that lets AI agents request missing capabilities on demand.

Build AI-powered frontends with NeuroLink's client SDKs featuring React hooks for streaming, a type-safe HTTP client, SSE and WebSocket transports, and a drop-in Vercel AI SDK adapter.

NeuroLink's 14 composable MCP enhancement modules turn raw Model Context Protocol into a production platform with intelligent tool routing, result caching, and request batching.