liter_llm

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# Elixir

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Universal LLM API client for Elixir. Access 165 LLM providers through a single interface with native BEAM concurrency, OTP integration, and idiomatic Elixir API.

## 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.
- **BEAM package** — Rustler NIF binding for OTP services.

## Installation

### Package Installation

Add to your `mix.exs` dependencies:

```elixir
def deps do
  [
    {:liter_llm, "~> 1.16.0"}
  ]
end
```

Then run:

```bash
mix deps.get
```

### System Requirements

- **Elixir 1.14+** and **Erlang/OTP 25+** required
- API keys via environment variables (e.g. `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`)

## Quick Start

### Basic Chat

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

```elixir
{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

request =
  Jason.encode!(%{
    model: "openai/gpt-4o-mini",
    messages: [%{role: "user", content: "Hello!"}]
  })

{:ok, result} = LiterLlm.defaultclient_chat_async(client, request)
IO.puts(Enum.at(result.choices, 0).message.content)
```

### Common Use Cases

#### Streaming Responses

Stream tokens in real time:

```elixir
{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

request =
  Jason.encode!(%{
    model: "openai/gpt-4o-mini",
    messages: [%{role: "user", content: "Count from 1 to 5."}],
    stream: true
  })

{:ok, stream} = LiterLlm.defaultclient_chat_stream(client, request)

Enum.each(stream, fn chunk ->
  content = get_in(chunk, [:choices, Access.at(0), :delta, :content])
  if content, do: IO.write(content)
end)

IO.puts("")
```

### Next Steps

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

## Features

### Supported Providers (165)

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/xberg-io/liter-llm/blob/main/schemas/providers.json)**

### Key Capabilities

- **Provider Routing** -- Single client for 165 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 165 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/xberg-io/liter-llm/blob/main/schemas/providers.json) for the full list.

## Proxy, MCP Server & Plugin

<details>
<summary><strong>Run the OpenAI-compatible proxy or the MCP server</strong></summary>

Beyond the SDK, the `liter-llm` CLI ships an OpenAI-compatible proxy and a Model Context Protocol (MCP) server:

```bash
brew install xberg-io/tap/liter-llm   # or: cargo install liter-llm-cli
liter-llm api --config liter-llm-proxy.toml   # OpenAI-compatible proxy
liter-llm mcp --transport stdio               # MCP tool server

# or run the proxy without installing:
docker run -p 4000:4000 -e LITER_LLM_MASTER_KEY=sk-your-key ghcr.io/xberg-io/liter-llm
```

To use the MCP server inside a coding agent, install the **liter-llm plugin** from the [`xberg-io/plugins`](https://github.com/xberg-io/plugins) marketplace — it auto-registers the server. See the [MCP server](https://docs.liter-llm.xberg.io/server/mcp-server/) and [proxy server](https://docs.liter-llm.xberg.io/server/proxy-server/) guides for configuration, CLI usage, and agent integration.

</details>

## Documentation

- **[Documentation](https://docs.liter-llm.xberg.io)** -- Full docs and API reference
- **[GitHub Repository](https://github.com/xberg-io/liter-llm)** -- Source, issues, and discussions
- **[Provider Registry](https://github.com/xberg-io/liter-llm/blob/main/schemas/providers.json)** -- 165 supported providers

## Part of Xberg.io

- [Xberg](https://github.com/xberg-io/xberg) — document intelligence: text, tables, metadata from 101 formats with optional OCR.
- [Xberg Enterprise](https://github.com/xberg-io/xberg-enterprise) — managed extraction API with SDKs, dashboards, and observability.
- [crawlberg](https://github.com/xberg-io/crawlberg) — web crawling and scraping with HTML→Markdown and headless-Chrome fallback.
- [html-to-markdown](https://github.com/xberg-io/html-to-markdown) — fast, lossless HTML→Markdown engine.
- [liter-llm](https://github.com/xberg-io/liter-llm) — universal LLM API client with native bindings for 14 languages and 165 providers.
- [tree-sitter-language-pack](https://github.com/xberg-io/tree-sitter-language-pack) — tree-sitter grammars and code-intelligence primitives.
- [alef](https://github.com/xberg-io/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/xberg-io/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/xberg-io/liter-llm/blob/main/LICENSE) for details.