npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2026 – Pkg Stats / Ryan Hefner

@sschepis/llm-wrapper

v0.3.5

Published

Unified LLM wrapper standardizing interactions across providers using the OpenAI Chat Completion format

Readme

@sschepis/llm-wrapper

A unified TypeScript library for interacting with multiple LLM providers through a single, standardized OpenAI-compatible interface. Switch providers with one line — get retries, streaming, tool use, and intelligent routing for free.

Features

  • 9 providers — OpenAI, Anthropic, Gemini, Vertex AI (Gemini + Anthropic), OpenRouter, DeepSeek, LM Studio, Ollama
  • Standardized interface — OpenAI Chat Completion format as the lingua franca
  • Intelligent routing — cost-based, latency-based, capability matching, load balancing, fallback chains
  • Circuit breaker — automatic health tracking with configurable thresholds and cooldowns
  • Streaming — full streaming support with AsyncIterable<StandardChatChunk>
  • Tool use — unified tool calling across all providers
  • Type-safe — Zod schemas with inferred TypeScript types
  • Lightweight — provider SDKs are optional peer dependencies; install only what you use
  • Dual format — ships ESM and CJS with full type declarations

Installation

npm install @sschepis/llm-wrapper

# Install only the provider SDKs you need
npm install openai                  # OpenAI, OpenRouter, DeepSeek, LM Studio, Ollama
npm install @anthropic-ai/sdk       # Anthropic, Vertex Anthropic
npm install @google/generative-ai   # Gemini
npm install @google-cloud/vertexai  # Vertex Gemini, Vertex Anthropic

Quick Start

Single Provider

import { UniversalLLM } from '@sschepis/llm-wrapper';

const client = await UniversalLLM.create({
  provider: 'anthropic',
  apiKey: process.env.ANTHROPIC_API_KEY!,
});

const response = await client.chat({
  model: 'claude-sonnet-4-20250514',
  messages: [{ role: 'user', content: 'Hello!' }],
});

console.log(response.choices[0].message.content);

Multi-Provider Router

import { LLMRouter, CostStrategy, CapabilityStrategy } from '@sschepis/llm-wrapper';

const router = await LLMRouter.create({
  endpoints: [
    {
      name: 'claude',
      provider: 'anthropic',
      model: 'claude-sonnet-4-20250514',
      config: { apiKey: process.env.ANTHROPIC_API_KEY! },
      costPer1kInput: 0.003,
      costPer1kOutput: 0.015,
      priority: 0,
    },
    {
      name: 'gpt',
      provider: 'openai',
      model: 'gpt-4o',
      config: { apiKey: process.env.OPENAI_API_KEY! },
      costPer1kInput: 0.0025,
      costPer1kOutput: 0.01,
      priority: 1,
    },
    {
      name: 'gemini',
      provider: 'gemini',
      model: 'gemini-2.0-flash',
      config: { apiKey: process.env.GEMINI_API_KEY! },
      costPer1kInput: 0.0001,
      costPer1kOutput: 0.0004,
      priority: 2,
    },
  ],
  strategy: [new CapabilityStrategy(), new CostStrategy()],
});

// Router picks the best endpoint automatically
const response = await router.chat({
  model: 'auto',
  messages: [{ role: 'user', content: 'Hello!' }],
});

Streaming

for await (const chunk of client.stream({
  model: 'claude-sonnet-4-20250514',
  messages: [{ role: 'user', content: 'Tell me a story' }],
})) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}

Tool Use

const response = await client.chat({
  model: 'claude-sonnet-4-20250514',
  messages: [{ role: 'user', content: 'What is the weather in NYC?' }],
  tools: [{
    type: 'function',
    function: {
      name: 'get_weather',
      description: 'Get the current weather for a city',
      parameters: {
        type: 'object',
        properties: {
          city: { type: 'string' },
        },
        required: ['city'],
      },
    },
  }],
});

if (response.choices[0].finish_reason === 'tool_calls') {
  const toolCall = response.choices[0].message.tool_calls![0];
  console.log(toolCall.function.name);       // 'get_weather'
  console.log(toolCall.function.arguments);  // '{"city":"NYC"}'
}

Supported Providers

| Provider | Provider Name | SDK Required | Notes | |---|---|---|---| | OpenAI | openai | openai | Reference implementation | | Anthropic | anthropic | @anthropic-ai/sdk | Full Messages API mapping | | Google Gemini | gemini | @google/generative-ai | Parts API mapping | | Vertex AI Gemini | vertex-gemini | @google-cloud/vertexai | Uses ADC auth | | Vertex AI Anthropic | vertex-anthropic | @anthropic-ai/sdk | Claude via Vertex | | OpenRouter | openrouter | openai | OpenAI-compatible | | DeepSeek | deepseek | openai | OpenAI-compatible | | LM Studio | lmstudio | openai | Local, OpenAI-compatible | | Ollama | ollama | openai | Local, OpenAI-compatible |

Local Providers

// Ollama (default: localhost:11434)
const client = await UniversalLLM.create({
  provider: 'ollama',
  apiKey: 'ollama',
  // baseUrl: 'http://localhost:11434/v1', // default
});

// LM Studio (default: localhost:1234)
const client = await UniversalLLM.create({
  provider: 'lmstudio',
  apiKey: 'lm-studio',
});

Router

The LLMRouter sits between your application and multiple provider endpoints, selecting the best one per request.

Routing Strategies

| Strategy | Type | Description | |---|---|---| | CapabilityStrategy | Filter | Removes endpoints that can't handle the request (tools, vision, context) | | CostStrategy | Select | Picks the cheapest endpoint based on estimated token cost | | LatencyStrategy | Select | Picks the endpoint with lowest observed latency | | PriorityStrategy | Select | Picks by priority number (lower = better), skips failed on fallback | | LoadBalanceStrategy | Select | Weighted round-robin distribution | | FallbackStrategy | Select | On retries, skips previously failed endpoints | | CustomStrategy | Select | Wraps a user-provided function |

Strategies compose into a pipeline. Filters run first, then selectors — first non-null result wins.

Circuit Breaker

The router tracks health per endpoint with a circuit breaker:

  • Closed (healthy) — requests flow normally, error rate tracked in rolling window
  • Open (broken) — requests skip this endpoint; transitions to half-open after cooldown
  • Half-open — allows one probe request; success closes, failure re-opens
const router = await LLMRouter.create({
  endpoints: [...],
  healthCheck: {
    windowSize: 60_000,     // 60s rolling window
    errorThreshold: 0.5,    // Trip at 50% error rate
    cooldownMs: 30_000,     // 30s before half-open probe
    minRequests: 5,         // Need 5+ requests before tripping
  },
});

Observability

router.events.on('route', ({ decision }) => {
  console.log(`Routed to ${decision.endpoint.name}: ${decision.reason}`);
});

router.events.on('fallback', ({ from, to, error }) => {
  console.warn(`Fallback: ${from.name} → ${to.name} (${error.code})`);
});

router.events.on('circuit:open', ({ endpoint }) => {
  alert(`Circuit breaker opened for ${endpoint.name}`);
});

router.events.on('request:complete', ({ endpoint, latencyMs, usage }) => {
  metrics.record(endpoint.name, latencyMs, usage?.total_tokens);
});

Utilities

Stream Aggregation

import { aggregateStream, teeStream } from '@sschepis/llm-wrapper';

// Collect stream into final response
const response = await aggregateStream(client.stream({ ... }));

// Yield chunks AND get final response
const { chunks, result } = teeStream(client.stream({ ... }));
for await (const chunk of chunks) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}
const final = await result;

Token Estimation

import { estimateTokens, validateContextWindow } from '@sschepis/llm-wrapper';

const tokens = estimateTokens(messages);
const { ok, remainingTokens } = validateContextWindow(messages, 'gpt-4o');

Message Truncation

import { truncateMessages } from '@sschepis/llm-wrapper';

const trimmed = truncateMessages(messages, 4000, {
  strategy: 'oldest',    // or 'middle'
  preserveSystem: true,  // keep system messages
});

Hooks

const client = await UniversalLLM.create({
  provider: 'openai',
  apiKey: process.env.OPENAI_API_KEY!,
  hooks: {
    onBeforeRequest: (params) => {
      console.log(`Sending ${params.messages.length} messages to ${params.model}`);
      return params;
    },
    onAfterResponse: (response) => {
      console.log(`Used ${response.usage.total_tokens} tokens`);
    },
    onError: (error) => {
      console.error(`LLM error: ${error.message}`);
    },
  },
});

Error Handling

All provider errors are normalized into LLMError with unified error codes:

import { LLMError, LLMErrorCode } from '@sschepis/llm-wrapper';

try {
  await client.chat({ ... });
} catch (err) {
  if (err instanceof LLMError) {
    switch (err.code) {
      case LLMErrorCode.RATE_LIMIT:       // 429 — retried automatically
      case LLMErrorCode.CONTEXT_EXCEEDED: // Message too long for model
      case LLMErrorCode.INVALID_API_KEY:  // 401
      case LLMErrorCode.MODEL_NOT_FOUND:  // 404
      case LLMErrorCode.PROVIDER_UNAVAILABLE: // 5xx — retried automatically
      case LLMErrorCode.CONTENT_FILTER:   // Safety filter triggered
    }
    console.log(err.provider);   // 'openai', 'anthropic', etc.
    console.log(err.statusCode); // HTTP status code
    console.log(err.retryable);  // Whether it was retried
  }
}

Architecture

@sschepis/llm-wrapper
├── core/
│   ├── types.ts          — Zod schemas + TypeScript types (OpenAI format)
│   ├── errors.ts         — LLMError + unified error codes
│   ├── base-provider.ts  — Abstract class: retry, hooks, validation
│   └── factory.ts        — createProvider(), UniversalLLM
├── providers/
│   ├── openai-provider.ts      — Reference implementation
│   ├── anthropic-provider.ts   — Messages API mapper
│   ├── gemini-provider.ts      — Parts API mapper
│   ├── vertex-gemini-provider.ts
│   ├── vertex-anthropic-provider.ts
│   └── openai-compat.ts        — OpenRouter, DeepSeek, LM Studio, Ollama
├── router/
│   ├── router.ts         — LLMRouter (routing + fallback + health)
│   ├── routing-engine.ts — Strategy pipeline
│   ├── strategies.ts     — 7 built-in strategies
│   ├── health-tracker.ts — Circuit breaker
│   ├── events.ts         — Typed event emitter
│   └── types.ts          — Router types
└── utils/
    ├── token-counter.ts      — Token estimation
    ├── stream-aggregator.ts  — Stream → response
    ├── model-registry.ts     — Model metadata
    └── truncation.ts         — Message truncation

Development

pnpm install
pnpm build        # Build ESM + CJS + types
pnpm test         # Run tests
pnpm test:watch   # Watch mode
pnpm typecheck    # Type checking only

License

MIT