Tinkuy Labs

AI Agent Infrastructure for Edge

The Almost-Zero Stack. Start serverless. Scale to zero.
Budget-guarded from day one. Production-ready from line one.

The Almost-Zero Journey

How your agent goes from zero to production without burning money.

Scale to Zero

Your agent sleeps when idle. Cloudflare Workers cold-start in <5ms. You pay nothing until someone talks to your agent. Not "serverless with a minimum" — actually zero.

On-Demand with Guards

When traffic comes, Sayay watches every token. $5/day budget? The agent auto-downgrades to a free model before burning credits. You set the ceiling — the agent respects it.

FinOps Intelligence Always

From the first request, you know what each user costs. Styrr auto-routes to the cheapest model that works. TideRAG runs on free-tier Vectorize. The stack is designed for cost-awareness, not just cost-avoidance.

The Stack

Four packages. Zero dependencies between them. Use one or all.

🧭

Styrr

@carloscortezcloud/styrr-llm

Multi-model LLM router. Ordered fallback chain. If model A rate-limits → try B → try C. Zero config.

const router = new StyrRouter({
models: [model_a, model_b, model_c],
apiKey
})

⚓

Sayay

@carloscortezcloud/sayay-guard

Budget guardrails. Set $X/day per user. Warn at 80%. Block at 95%. Auto-degrade model when budget runs low.

const guard = new SayayGuard({
budget: { dailyUsd: 5.0 }
}) // never exceed

🌊

Tinkuy

@carloscortezcloud/tinkuy-agent

Core

The agent loop. 200 lines. Call LLM → parse tool_calls → execute → feed back → repeat. Provider-agnostic. Edge-native. Pluggable.

const agent = new Agent({ router, guard, tools, systemPrompt });
const result = await agent.run("What are my AWS costs?");
// 3 iterations | 2 tools called | 7s | $0.00

🌊

TideRAG

@carloscortezcloud/tiderag

Vectorize + Workers AI + D1

RAG pipeline on Cloudflare free tier. Ingest documents → embed → query. 5M vectors, free embeddings, edge latency. Everyone else charges $70+/mo for this.

monthly

vectors free

~20ms

latency

285

lines

Install

Pick what you need. Each package is independent.

🧭 Styrr — LLM Router

npm install @carloscortezcloud/styrr-llm

⚓ Sayay — Budget Guard

npm install @carloscortezcloud/sayay-guard

🌊 Tinkuy — Agent Framework

npm install @carloscortezcloud/tinkuy-agent

🌊 TideRAG — Edge RAG

npm install @carloscortezcloud/tiderag

⚡ Full Stack (all 4)

npm install @carloscortezcloud/tinkuy-agent @carloscortezcloud/styrr-llm @carloscortezcloud/sayay-guard @carloscortezcloud/tiderag

Copy for your Coding Agent

Paste this into Kiro, Claude, Cursor, or GitHub Copilot. It knows how to set up the full stack.

agent-prompt.md

# Tinkuy Labs — AI Agent Stack Setup

## Install
npm install @carloscortezcloud/tinkuy-agent @carloscortezcloud/styrr-llm @carloscortezcloud/sayay-guard

## Create an agent with multi-model fallback + budget guards:

```typescript
import { Agent, defineTool } from '@carloscortezcloud/tinkuy-agent';
import { StyrRouter } from '@carloscortezcloud/styrr-llm';
import { SayayGuard, MemoryStorage } from '@carloscortezcloud/sayay-guard';

// Define a tool
const myTool = defineTool({
  name: 'search',
  description: 'Search for information',
  parameters: { type: 'object', properties: { query: { type: 'string' } }, required: ['query'] },
  execute: async (args) => { /* your logic */ return { result: 'found' }; },
});

// Create agent
const agent = new Agent({
  router: new StyrRouter({
    apiKey: process.env.OPENROUTER_API_KEY,
    models: [
      { id: 'nvidia/nemotron-3-ultra-550b-a55b:free' },
      { id: 'google/gemma-4-31b-it:free' },
    ],
  }),
  guard: new SayayGuard({
    storage: new MemoryStorage(),
    budget: { dailyUsd: 5.0 },
  }),
  tools: [myTool],
  systemPrompt: 'You are a helpful assistant.',
  maxIterations: 5,
});

const result = await agent.run('Hello!');
console.log(result.text); // Agent's response
console.log(result.toolsUsed); // Which tools were called
```

## Key concepts:
- StyrRouter: tries models in order, auto-fallbacks on 429/404/402
- SayayGuard: blocks/warns when budget exceeded (set per user/day/month)
- Agent: tool loop (LLM → tool_calls → execute → feed back → repeat)
- defineTool: type-safe tool factory with name, description, parameters, execute

## Links:
- GitHub: https://github.com/breakingthecloud/tinkuylabs
- npm: @carloscortezcloud/tinkuy-agent, styrr-llm, sayay-guard, tiderag
- Docs: https://tinkuylabs.finoptix.dev

Start in 30 seconds

# Install
npm install @carloscortezcloud/tinkuy-agent @carloscortezcloud/styrr-llm

# Create agent (10 lines)
import { Agent, defineTool } from '@carloscortezcloud/tinkuy-agent'
import { StyrRouter } from '@carloscortezcloud/styrr-llm'

const agent = new Agent({
  router: new StyrRouter({ apiKey, models: [{ id: 'nvidia/nemotron-3-ultra:free' }] }),
  tools: [myTool],
  systemPrompt: 'You are a helpful assistant.'
})

const result = await agent.run('Hello!')
// → Agent calls tools, reasons, responds. $0.00 cost.

How we compare

	Tinkuy Labs	Strands Agents	LangChain
Bundle size	~1KB	Large	Large
Edge-native	✓ CF Workers	✗ Lambda	✗ Node
Multi-model fallback	✓ Built-in	✗	✗
Budget guardrails	✓ Built-in	✗	✗
RAG included	✓ $0/mo	✗	✗ (Pinecone $70)
Zero dependencies	✓	✗	✗
Monthly infrastructure	$0	$0 + Bedrock	$70+

Used in production at

SOFE — Open FinOps Engine

100+ findings analyzed • 6 tools • RAG knowledge base • Free model routing