Java

Universal LLM API client for Java. Access 165 LLM providers through a single type-safe interface with Foreign Function & Memory API integration, async support, and native performance.

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.
• Java package — type-safe FFM binding for JVM services.

Installation

Package Installation

Install via one of the supported package managers:

Maven:

<dependency>
    <groupId>io.xberg.literllm</groupId>
    <artifactId>liter-llm</artifactId>
    <version>1.11.0</version>
</dependency>

Gradle:

implementation 'io.xberg.literllm:liter-llm:1.11.0'

System Requirements

• Java 21+ required (Panama FFM API)
• 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:

import io.xberg.literllm.*;
import java.util.List;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var request = ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o")
                .withMessages(List.of(
                    new Message.User(new UserMessage(UserContent.of("Hello!"), null))
                ))
                .build();
            var response = client.chat(request);
            System.out.println(response.choices().getFirst().message().content());
        }
    }
}

Common Use Cases

Streaming Responses

Stream tokens in real time:

import io.xberg.literllm.*;
import java.util.List;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var request = ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o-mini")
                .withMessages(List.of(
                    new Message.User(new UserMessage(UserContent.of("Hello"), null))
                ))
                .build();
            var stream = client.chatStream(request);
            var iterator = stream.iterator();
            while (iterator.hasNext()) {
                var chunk = iterator.next();
                var delta = chunk.choices().getFirst().delta().content();
                if (delta != null) System.out.print(delta);
            }
            System.out.println();
        }
    }
}

Next Steps

• Provider Registry - Full list of supported providers
• GitHub Repository - Source, issues, and discussions

Features

Supported Providers (165)

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

Complete Provider List

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 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:

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

See the provider registry for the full list.

Proxy, MCP Server & Plugin

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

Documentation

• Documentation -- Full docs and API reference
• GitHub Repository -- Source, issues, and discussions
• Provider Registry -- 165 supported providers

Part of Xberg.io

• Xberg — document intelligence: text, tables, metadata from 91+ formats with optional OCR.
• Xberg Enterprise — managed extraction API with SDKs, dashboards, and observability.
• crawlberg — web crawling and scraping with HTML→Markdown and headless-Chrome fallback.
• html-to-markdown — fast, lossless HTML→Markdown engine.
• liter-llm — universal LLM API client with native bindings for 14 languages and 165 providers.
• tree-sitter-language-pack — tree-sitter grammars and code-intelligence primitives.
• alef — the polyglot binding generator that produces every per-language binding across the 5 polyglot repos.
• Discord — community, roadmap, announcements.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Join our Discord community for questions and discussion.

License

MIT -- see LICENSE for details.