npm package: @kreuzberg/liter-llm-native
Description: Native NAPI-RS bindings for liter-llm — high-performance LLM client powered by Rust
Latest version: 1.2.0

# TypeScript (Node.js)

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Universal LLM API client for TypeScript and Node.js. Access 143+ LLM providers through a single interface with native NAPI-RS bindings, async/await support, streaming, tool calling, and full TypeScript type definitions.


## Installation

### Package Installation


Install via one of the supported package managers:



**npm:**

```bash
npm install @kreuzberg/liter-llm
```




**pnpm:**

```bash
pnpm add @kreuzberg/liter-llm
```




**yarn:**

```bash
yarn add @kreuzberg/liter-llm
```





### System Requirements


- **Node.js 22+** required (NAPI-RS native bindings)
- API keys via environment variables (e.g. `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`)

### Platform Support

Pre-built binaries available for:

- macOS (arm64, x64)
- Linux (x64)
- Windows (x64)




## Quick Start

### Basic Chat

Send a message to any provider using the `provider/model` prefix:

```typescript
import { LlmClient } from "@kreuzberg/liter-llm";

const client = new LlmClient({ apiKey: process.env.OPENAI_API_KEY! });
const response = await client.chat({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);
```


### Common Use Cases


#### Streaming Responses

Stream tokens in real time:

```typescript
import { LlmClient } from "@kreuzberg/liter-llm";

const client = new LlmClient({ apiKey: process.env.OPENAI_API_KEY! });
const chunks = await client.chatStream({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Tell me a story" }],
});

for (const chunk of chunks) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}
console.log();
```




#### Tool Calling

Define and invoke tools:

```typescript
import { LlmClient } from "@kreuzberg/liter-llm";

const client = new LlmClient({ apiKey: process.env.OPENAI_API_KEY! });

const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get the current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "What is the weather in Berlin?" }],
  tools,
});

for (const call of response.choices[0]?.message?.toolCalls ?? []) {
  console.log(`Tool: ${call.function.name}, Args: ${call.function.arguments}`);
}
```




### Next Steps

- **[Provider Registry](https://github.com/kreuzberg-dev/liter-llm/blob/main/schemas/providers.json)** - Full list of supported providers
- **[GitHub Repository](https://github.com/kreuzberg-dev/liter-llm)** - Source, issues, and discussions



## NAPI-RS Implementation Details

### Native Performance

This binding uses NAPI-RS to provide native Node.js bindings with:

- **Zero-copy data transfer** between JavaScript and Rust layers
- **Async by default** — all LLM calls return Promises backed by Tokio
- **Binary-compatible** pre-built native modules across platforms
- **TypeScript definitions** generated automatically from Rust types

### Threading Model

- LLM calls are non-blocking — Tokio async runtime handles concurrency
- Streaming responses use Node.js async iterators backed by Tokio streams
- CPU-bound work runs in `spawn_blocking` to avoid blocking the event loop

### Memory Management

- API keys are wrapped in `secrecy::SecretString` and never logged
- Streaming buffers are released as soon as each chunk is consumed
- Provider registry is compiled into the binary — no runtime disk access



## Features

### Supported Providers (143+)

Route to any provider using the `provider/model` prefix convention:

| Provider | Example Model |
|----------|--------------|
| **OpenAI** | `openai/gpt-4o`, `openai/gpt-4o-mini` |
| **Anthropic** | `anthropic/claude-3-5-sonnet-20241022` |
| **Groq** | `groq/llama-3.1-70b-versatile` |
| **Mistral** | `mistral/mistral-large-latest` |
| **Cohere** | `cohere/command-r-plus` |
| **Together AI** | `together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo` |
| **Fireworks** | `fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct` |
| **Google Vertex** | `vertexai/gemini-1.5-pro` |
| **Amazon Bedrock** | `bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0` |

**[Complete Provider List](https://github.com/kreuzberg-dev/liter-llm/blob/main/schemas/providers.json)**

### Key Capabilities

- **Provider Routing** -- Single client for 143+ LLM providers via `provider/model` prefix
- **Local LLMs** — Connect to locally-hosted models via Ollama, LM Studio, vLLM, llama.cpp, and other local inference servers
- **Unified API** -- Consistent `chat`, `chat_stream`, `embeddings`, `list_models` interface

- **Streaming** -- Real-time token streaming via `chat_stream`


- **Tool Calling** -- Function calling and tool use across all supporting providers

- **Type Safe** -- Schema-driven types compiled from JSON schemas
- **Secure** -- API keys never logged or serialized, managed via environment variables
- **Observability** -- Built-in [OpenTelemetry](https://opentelemetry.io/docs/specs/semconv/gen-ai/) with GenAI semantic conventions
- **Error Handling** -- Structured errors with provider context and retry hints

### Performance

Built on a compiled Rust core for speed and safety:

- **Provider resolution** at client construction -- zero per-request overhead
- **Configurable timeouts** and connection pooling
- **Zero-copy streaming** with SSE and AWS EventStream support
- **API keys** wrapped in secure memory, zeroed on drop



## Provider Routing

Route to 143+ providers using the `provider/model` prefix convention:

```text
openai/gpt-4o
anthropic/claude-3-5-sonnet-20241022
groq/llama-3.1-70b-versatile
mistral/mistral-large-latest
```

See the [provider registry](https://github.com/kreuzberg-dev/liter-llm/blob/main/schemas/providers.json) for the full list.



## Proxy Server

liter-llm also ships as an OpenAI-compatible proxy server with Docker support:

```bash
docker run -p 4000:4000 -e LITER_LLM_MASTER_KEY=sk-your-key ghcr.io/kreuzberg-dev/liter-llm
```

See the [proxy server documentation](https://docs.liter-llm.kreuzberg.dev/server/proxy/) for configuration, CLI usage, and MCP integration.


## Documentation

- **[Documentation](https://docs.liter-llm.kreuzberg.dev)** -- Full docs and API reference
- **[GitHub Repository](https://github.com/kreuzberg-dev/liter-llm)** -- Source, issues, and discussions
- **[Provider Registry](https://github.com/kreuzberg-dev/liter-llm/blob/main/schemas/providers.json)** -- 143 supported providers

Part of [kreuzberg.dev](https://kreuzberg.dev).

## Contributing

Contributions are welcome! See [CONTRIBUTING.md](https://github.com/kreuzberg-dev/liter-llm/blob/main/CONTRIBUTING.md) for guidelines.

Join our [Discord community](https://discord.gg/xt9WY3GnKR) for questions and discussion.

## License

MIT -- see [LICENSE](https://github.com/kreuzberg-dev/liter-llm/blob/main/LICENSE) for details.