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claude mcp add juspay-neurolink-github -- npx -y @juspay/neurolink@13.12.1Bir terminalde çalıştırın.
{ "mcpServers": { "juspay-neurolink-github": { "command": "npx", "args": [ "-y", "@juspay/[email protected]" ] } }}claude_desktop_config.json
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The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across three inference types: generate, stream, and a calibrated decide (via TypeSafe Jev). MCP-native, voice (TTS/STT/realtime), RAG, memory, file processors. Powers Tara, Yama and Clairvoyance at Juspay.
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);
}
}
// Or skip text entirely: a calibrated decision, not a token stream
const decision = await pipe.tryDecide({
// null if no decision provider is set
state: { ticket: "Refund request, $42, first occurrence" },
questions: {
autoApprove: {
type: "boolean",
instructions: "Approve without human review.",
},
},
});
// decision?.answers.autoApprove.probability -> 0.91
→ Docs · → Quick Start · → npm · → Blog
NeuroLink is the pipe layer of an AI nervous system. Providers — OpenAI, Anthropic, Google, AWS, Azure, Mistral, local runtimes like Ollama, and dozens more — are the neurons: each generates a different kind of intelligence, at a different cost and latency. NeuroLink is the vascular layer that carries that intelligence, as a stream, to the applications — the organs — that consume it, across three inference types: generate and stream produce text, decide produces a calibrated boolean/choice/score judgment instead. A curated model registry (64 models, 132 aliases) backs metadata, routing, and context-window checks out of the box, and hundreds more models are reachable through aggregator providers — 100+ via LiteLLM, 300+ via OpenRouter.
Extracted from production systems at Juspay, NeuroLink provides a practical, TypeScript-first way to plug any application into that nervous system. Switch which neuron answers a request with a single parameter change — OpenAI, Anthropic, Google, AWS Bedrock, Azure, a local runtime, or any provider you add. decide is the third inference type — a typed, calibrated judgment instead of text — for the model-routing and gating decisions generate/stream were never meant to make, powered by a purpose-built decision model (TypeSafe Jev, Perplexity's hosted Decisions API, Cloudflare's hosted Clef, Subquadratic's hosted SubQ Snap, OpenAI's hosted Decisions API, or the open-weights Laya or XOR) rather than a general-purpose LLM: with Jev, routing decisions land in ~400ms for about $0.00002, instead of a full generation call.
Why NeuroLink? Three genuine inference types, not one dressed up three ways — generate and stream produce text; decide produces a calibrated boolean/choice/score judgment, and which types a provider serves is declared per-provider via inferenceKinds rather than inferred from behavior. Every neuron plugs into the same pipe, including 3 fully local runtimes (Ollama, LM Studio, llama.cpp) with per-request credential overrides, and MCP support covers all 4 transports (stdio, HTTP, SSE, WebSocket). Every AI-driven optimization the pipe performs — model routing, context compaction, tool selection — fails open: no key configured behaves exactly like NeuroLink without it, and routing uses asymmetric confidence thresholds (upgrade at 0.3, downgrade at 0.6) rather than a single cutoff, because a wrong downgrade costs more than a wrong upgrade. Switch providers with a single parameter change, leverage built-in tools plus any MCP-compliant tool server, 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 →
// decide() — a third inference type: calibrated judgments, not text (next)
// Enable with a decision provider's key, e.g. TYPESAFE_API_KEY (or AI_GATEWAY_API_KEY via Vercel AI Gateway).
import { NeuroLink, readDecisionChoice } from "@juspay/neurolink";
const neurolink = new NeuroLink();
const result = await neurolink.tryDecide({
// null if no decision provider is configured
state: ticketText,
questions: {
team: {
type: "choice",
instructions: "Which team should handle this?",
criteria: { billing: "Payments", technical: "Bugs", sales: "Pricing" },
},
urgent: { type: "boolean", instructions: "Is this urgent?" },
},
});
const team = result && readDecisionChoice(result.answers, "team");
if (team && team.confidence > 0.7) {
route(team.choice); // "billing" | "technical" | "sales", plus a full ranking
}
// 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"],
});
generate() and stream() call.conversation:titleGenerated event + NEUROLINK_TITLE_PROMPT custom titles. → Guideschema + output.format: "json". → GuideNeuroLink now supports decision models — a third inference type, served by hosted providers and by open-weights models you can run yourself.
decide sits alongside generate and stream. Instead of tokens, a decision
model takes one state plus a map of named typed questions and returns one
typed, calibrated answer per question, all in a single parallel pass — no
text output anywhere, so nothing has to be parsed back out of prose.
Public API: neurolink.decide() and the fail-open neurolink.tryDecide()
(returns null instead of throwing). The CLI has a thin wrapper over decide(),
neurolink decide [state] — see the CLI reference.
A score is probability-weighted, so it can land between rubric levels —
useful for sorting a queue, not just bucketing it.
The model is fast, cheap and calibrated, but ~68% accurate (see the trade-off below). That combination fits work that is batched, gated and reversible — where a wrong answer is caught by a threshold or a human, not shipped to a user.
Do not use it for a final answer a user reads, an irreversible action with no
confirmation step, or anything needing a rationale — a decision carries a
probability, never an explanation. Those belong to generate.
Set a decision provider's key. For TypeSafe Jev
(AIProviderName.TYPESAFE, aliases jev / typesafe-ai) that is
TYPESAFE_API_KEY, and it needs nothing else. It is also reachable through the
Vercel AI Gateway via AI_GATEWAY_API_KEY; force one transport with
TYPESAFE_TRANSPORT=direct|gateway. Per-request credentials work as they do for
every other provider.
Laya is a decision provider —
Convai Innovations' Apache-2.0, open-weights "System One" model, for
when you'd rather run the decision model on your own infrastructure than call a
hosted one. There is no built-in endpoint: set LAYA_API_KEY and
LAYA_BASE_URL to point at a Laya server you run yourself, or a LiteLLM proxy
with a pass-through route to one (or pass credentials.laya to
new NeuroLink({ credentials }), or per call). Its encoders read a much
shorter state than TypeSafe's — about 768 tokens on the default
typed-decisions checkpoint (320 on english/auto), against TypeSafe's
~33,000 — so it fits short, structured decisions rather than long context.
Every built-in consumer below asks for the first configured decision provider,
in the order TypeSafe, Laya, XOR, SubQ, Perplexity, Cloudflare Clef, OpenAI Decisions, ScaleDown: with
TypeSafe and Laya both configured, TypeSafe runs; with only Laya's key and base URL
set, Laya runs.
XOR is Juspay's Apache-2.0,
open-weights decision model (xor-latest for text, xor-omni for an image, audio and value extraction); setup is on
Hugging Face. It has no built-in endpoint
either: set XOR_API_KEY and XOR_BASE_URL to the origin of a deployment or of
a LiteLLM proxy route (or pass credentials.xor). XOR counts only with both its
key and base URL set, and a call can always name it with provider: "xor". It
accepts one image and one audio clip (WAV, FL
The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across three inference types: generate, stream, and a calibrated decide (via TypeSafe Jev). MCP-
{ "mcpServers": { "juspay-neurolink-github": { "command": "npx", "args": [ "-y", "@juspay/[email protected]" ] } }}Genel bakış
The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across three inference types: generate, stream, and a calibrated decide (via TypeSafe Jev). MCP-native, voice (TTS/STT/realtime), RAG, memory, file processors. Powers Tara, Yama and Clairvoyance at Juspay.
Araçlar
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SSS
Bu sayfadan Cursor, Claude Desktop, Claude Code veya VS Code için stdio yapılandırmasını kopyalayın. PlugMCP proxy'sini değil, yayıncının kurulumunu kullanır.
Bu kayıtta gerekli bir ortam değişkeni listelenmiyor.
Neurolink yerel olarak stdio üzerinden çalışır.
Son kontrol: 2026-10-27. The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across three inference types: generate, stream, and a calibrated decide (via TypeSafe Jev). MCP-native, voice (TTS/STT/realtime), RAG, memory, file processors. Powers Tara, Yama and Clairvoyance at Juspay.