liter-llm

name	liter-llm
description	Universal LLM API client for 143 providers with native bindings for 14 languages. Use when writing code that calls LLM APIs via liter-llm in Python, TypeScript, Rust, Go, Java, C#, Ruby, PHP, Elixir, WASM, or C, when running the OpenAI-compatible proxy, or when calling LLMs through the MCP server. Covers chat, streaming, tool calling, embeddings, image generation, speech, transcription, moderation, web search, OCR, reranking, provider routing, middleware, and configuration.
license	MIT
metadata	{"author":"kreuzberg-dev","version":"0.1.0","repository":"https://github.com/xberg-io/liter-llm"}

Liter-LLM Universal LLM Client

Liter-LLM is a universal LLM API client with a Rust core and native bindings for Python, TypeScript/Node.js, Go, Java, C#, Ruby, PHP, Elixir, WebAssembly, and C (FFI). One unified interface reaches 143 providers (OpenAI, Anthropic, Google Gemini, Groq, Mistral, Cohere, AWS Bedrock, Azure, and many more).

Capability map

• Modalities — chat completions, streaming, tool/function calling, structured outputs, embeddings, image generation, audio (speech, transcription, translation), moderation, web search, OCR, reranking, batch, and file operations.
• Provider routing — provider/model prefix selects the backend (openai/gpt-4o, anthropic/claude-sonnet-4-20250514, google/gemini-2.0-flash). Set model_hint to skip the prefix.
• Middleware — response caching, rate limiting (RPM/TPM), cost tracking, budget enforcement (hard / soft), fallback chains, circuit-breaker cooldown, and background health checks.
• Search providers (12) and OCR providers (4) — first-class search and ocr methods routed by the same prefix convention.
• Proxy server — liter-llm api exposes an OpenAI-compatible gateway with 22 REST endpoints, virtual API keys, budgets, and SSE streaming.
• MCP server — liter-llm mcp exposes 22 tools mirroring the proxy endpoints, for MCP-compatible clients (Claude Code, Claude Desktop).
• 14 language bindings — same surface, language-native naming (snake_case for Python/Rust/Ruby/Go/Elixir/PHP, camelCase for TS/Node/WASM/C#/Java).

When to use the MCP server vs the SDK vs the proxy

Use the …	When you want to …	Entry point
MCP server	Let the agent call LLM APIs directly as tools, with no glue code	liter-llm mcp --transport stdio (this plugin auto-registers it)
SDK / binding	Write application code that calls LLMs in a specific language	pip install liter-llm, cargo add liter-llm, etc.
Proxy	Give many apps/teams a shared OpenAI-compatible endpoint with keys, budgets, and rate limits	liter-llm api --config liter-llm-proxy.toml

Rule of thumb: reach for the MCP server inside an agent session; the SDK when building software; the proxy when centralizing access for multiple consumers.

Installation

CLI (proxy + MCP server)

# Homebrew (macOS / Linux)
brew install xberg-io/tap/liter-llm

# or run it without a persistent install (the CLI proxy package self-installs the binary)
npx @kreuzberg/liter-llm-cli --help
uvx --from liter-llm-cli liter-llm --help

# or download a prebuilt binary from the latest GitHub release:
#   https://github.com/xberg-io/liter-llm/releases/latest

# or build from source
cargo install --git https://github.com/xberg-io/liter-llm liter-llm-cli

# or Docker (35MB image)
docker pull ghcr.io/xberg-io/liter-llm

Language bindings

Language	Install
Python	pip install liter-llm
Node.js	pnpm add @kreuzberg/liter-llm-node
Rust	cargo add liter-llm
Go	go get github.com/xberg-io/liter-llm/packages/go
Ruby	gem install liter_llm
PHP	composer require xberg-io/liter-llm
C#	dotnet add package LiterLlm
WASM	pnpm add @kreuzberg/liter-llm-wasm

Quick start (Python, async)

import asyncio
import os
from liter_llm import LlmClient

async def main() -> None:
    client = LlmClient(api_key=os.environ["OPENAI_API_KEY"])
    response = await client.chat(
        model="openai/gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}],
    )
    print(response.choices[0].message.content)

asyncio.run(main())

Provider routing

The prefix before / selects the provider:

await client.chat(model="openai/gpt-4o", messages=[...])
await client.chat(model="anthropic/claude-sonnet-4-20250514", messages=[...])
await client.chat(model="google/gemini-2.0-flash", messages=[...])
await client.chat(model="groq/llama3-70b", messages=[...])

API keys come from environment variables: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, GROQ_API_KEY, MISTRAL_API_KEY, CO_API_KEY, and AWS credentials for Bedrock.

Configuration

Pass options to the constructor, or create a liter-llm.toml (SDK) / liter-llm-proxy.toml (proxy) — both auto-discover from the cwd upward.

# liter-llm.toml
api_key = "${OPENAI_API_KEY}"
model_hint = "openai"
timeout_secs = 120
max_retries = 5

[cache]
max_entries = 512
ttl_seconds = 600

[budget]
global_limit = 50.0
enforcement = "hard"

[rate_limit]
rpm = 60
tpm = 100000

Option	Default	Description
api_key	required	Provider key, wrapped in SecretString (never logged).
base_url	from registry	Override the provider base URL.
model_hint	none	Pre-resolve a provider, skipping the prefix lookup.
timeout	60s	Request timeout.
max_retries	3	Retries on 429/5xx with exponential backoff.
cache	none	max_entries, ttl_seconds.
budget	none	global_limit, model_limits, enforcement.
rate_limit	none	rpm, tpm.
cost_tracking	false	Per-request cost tracking.
tracing	false	OpenTelemetry spans.

Proxy server

liter-llm api --config liter-llm-proxy.toml

22 OpenAI-compatible endpoints, model routing by prefix, virtual API keys, per-key RPM/TPM limits, cost tracking, budget enforcement, response caching, SSE streaming, and an OpenAPI 3.1 spec at /openapi.json.

MCP server

liter-llm mcp --transport stdio   # for Claude Code / Claude Desktop
liter-llm mcp --transport http --port 3001

This plugin auto-registers the stdio server via scripts/mcp-launch.sh. The 22 tools mirror the proxy endpoints: chat, embed, generate_image, speech, transcribe, moderate, rerank, search, ocr, list_models; file ops (create_file, list_files, retrieve_file, delete_file, file_content); batch ops (create_batch, list_batches, retrieve_batch, cancel_batch); and Responses API (create_response, retrieve_response, cancel_response).

Common pitfalls

1. Provider prefix is required — use "provider/model" unless model_hint is set, or routing fails.
2. API keys are SecretString — never logged or serialized. Read from env vars; never hardcode.
3. Python methods are async — await inside an async context.
4. Naming conventions differ — camelCase for TS/Node/WASM/C#/Java, snake_case elsewhere.
5. Streaming chunks may have null content — null-check chunk.choices[0].delta.content before use.
6. Budget modes — hard rejects over-budget requests; soft logs and allows.

Additional resources

• Upstream docs: https://docs.liter-llm.xberg.io
• GitHub: https://github.com/xberg-io/liter-llm