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Universal LLM API client for Rust. Access 143 LLM providers through a single unified interface. Async/await with Tokio, streaming via BoxStream, composable Tower middleware stack, and compile-time type safety.

## What This Package Provides

- **One provider surface** — chat, streaming, embeddings, images, audio, search, OCR, tools, and structured output across the provider registry.
- **Provider/model routing** — call models with the `provider/model` convention and keep provider-specific request code out of application paths.
- **Production controls** — retries, fallback, rate limits, cache layers, budgets, health checks, OpenTelemetry spans, and redacted secrets.
- **Same core as every binding** — Rust, Python, Node.js, Go, Java, PHP, Ruby, .NET, Elixir, WASM, Kotlin Android, Swift, Dart, Zig, and C FFI use the same Rust implementation.
- **Rust crate** — canonical async client with Tower middleware and Tokio integration.

## Installation

### Package Installation

### System Requirements

- See [Installation Guide](https://github.com/kreuzberg-dev/liter-llm#installation) for requirements

## Quick Start

### Basic Chat

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

```rust
use liter_llm::{
    ChatCompletionRequest, ClientConfigBuilder, DefaultClient, LlmClient,
    Message, UserContent, UserMessage,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: vec![Message::User(UserMessage {
            content: UserContent::Text("Hello!".into()),
            name: None,
        })],
        ..Default::default()
    };

    let response = client.chat(request).await?;
    if let Some(choice) = response.choices.first() {
        println!("{}", choice.message.content.as_deref().unwrap_or(""));
    }
    Ok(())
}
```

### Common Use Cases

#### Streaming Responses

Stream tokens in real time:

```rust
use futures::StreamExt;
use liter_llm::{
    ChatCompletionRequest, ClientConfigBuilder, DefaultClient, LlmClient,
    Message, UserContent, UserMessage,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: vec![Message::User(UserMessage {
            content: UserContent::Text("Tell me a story".into()),
            name: None,
        })],
        ..Default::default()
    };

    let mut stream = client.chat_stream(request).await?;
    while let Some(chunk) = stream.next().await {
        let chunk = chunk?;
        if let Some(choice) = chunk.choices.first() {
            if let Some(content) = &choice.delta.content {
                print!("{content}");
            }
        }
    }
    println!();
    Ok(())
}
```

### 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

## 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-server/) 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

- [Kreuzberg](https://github.com/kreuzberg-dev/kreuzberg) — document intelligence: text, tables, metadata from 91+ formats with optional OCR.
- [Kreuzberg Cloud](https://github.com/kreuzberg-dev/kreuzberg-cloud) — managed extraction API with SDKs, dashboards, and observability.
- [kreuzcrawl](https://github.com/kreuzberg-dev/kreuzcrawl) — web crawling and scraping with HTML→Markdown and headless-Chrome fallback.
- [html-to-markdown](https://github.com/kreuzberg-dev/html-to-markdown) — fast, lossless HTML→Markdown engine.
- [liter-llm](https://github.com/kreuzberg-dev/liter-llm) — universal LLM API client with native bindings for 14 languages and 143 providers.
- [tree-sitter-language-pack](https://github.com/kreuzberg-dev/tree-sitter-language-pack) — tree-sitter grammars and code-intelligence primitives.
- [alef](https://github.com/kreuzberg-dev/alef) — the polyglot binding generator that produces every per-language binding across the 5 polyglot repos.
- [Discord](https://discord.gg/xt9WY3GnKR) — community, roadmap, announcements.

## 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.
