litellm-rs

A high-performance self-hosted LLM gateway with a stable OpenAI-compatible HTTP contract. The litellm-rs crate is the gateway's reusable Rust kernel, with narrower support policies for runtime-backed APIs and legacy compatibility adapters.

Features

• 60+ runtime-wired providers - OpenAI, Anthropic, AWS Bedrock, Mistral, Cloudflare, plus 50+ OpenAI-compatible providers via the Tier 1 catalog. See Provider Support for the full matrix.
• Stable OpenAI-Compatible API - Versioned inference contract served at GET /openapi.json
• Admin control-plane OpenAPI - Auth, keys, teams, budgets, routing, ledger, and provider APIs at GET /admin/openapi.json
• Measured Performance - Reproducible gateway-overhead benchmark methodology
• Intelligent Routing - Load balancing, failover, cost optimization
• Gateway Controls - Default-on prompt-injection guardrails, configured IP access, auth, rate limiting, deterministic caching, metrics, and health endpoints

Quick Start: Self-Hosted Gateway

Run the primary supported product from source:

git clone https://github.com/majiayu000/litellm-rs.git
cd litellm-rs
cp config/gateway.dev.yaml.example config/gateway.yaml
cargo run --bin gateway

Or install the gateway binary:

cargo install litellm-rs --bin gateway
mkdir -p config
curl -L https://raw.githubusercontent.com/majiayu000/litellm-rs/main/config/gateway.dev.yaml.example -o config/gateway.yaml
gateway

The development config starts without provider credentials or auth secrets and uses the local vllm catalog provider. Use config/gateway.yaml.example for production-style deployments with real provider keys and auth enabled. Default features include SQLite storage, which satisfies the gateway binary's storage requirement.

The gateway serves its stable inference contract at GET /openapi.json; the versioned source is docs/openapi/inference.json. The admin control-plane contract is served at GET /admin/openapi.json (admin-authenticated) from docs/openapi/admin.json.

Supported Product Surfaces

Rust Crate

[dependencies]
litellm-rs = { version = "0.6", default-features = false, features = ["lite"] }

No make step is required for crate consumers.

Library Example

use litellm_rs::{completion, user_message, system_message};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let response = completion(
        "gpt-4",
        vec![
            system_message("You are a helpful assistant."),
            user_message("Hello!"),
        ],
        None,
    ).await?;

    println!("{}", response.choices[0].message.content.as_ref().unwrap());
    Ok(())
}

Gateway Configuration

The gateway router config maps these fields into the runtime router:

• router.strategy selects the deployment routing strategy.
• router.circuit_breaker.failure_threshold controls consecutive failures before cooldown.
• router.circuit_breaker.recovery_timeout controls cooldown duration in seconds.
• router.circuit_breaker.min_requests sets the sample size required before cooldown.
• router.circuit_breaker.success_threshold sets the successes required to recover from cooldown.
• router.load_balancer.health_check_enabled enables pre-call deployment health checks.

Active provider probes are opt-in: a provider's health_check must differ from the defaults. Native probes are limited to provider types whose configured deployment is unambiguously a chat model (currently Anthropic and GitHub Copilot); they send a one-token request and may incur provider charges. OpenAI, Bedrock, OpenAI-compatible, Vertex AI, FalAI, and other multi-capability providers require a custom health_check.endpoint. Without an active probe policy, readiness remains fail-closed (Unknown).

router.load_balancer.sticky_sessions and router.load_balancer.session_timeout are reserved for future session affinity. Non-default values fail config validation until runtime affinity is implemented.

Gateway YAML can publish stable model names and deterministic primary/fallback tiers:

providers:
  - name: openai-primary
    provider_type: openai
    api_key: "${OPENAI_API_KEY}"
    models: [gpt-4o]
    priority: 0
  - name: openai-fallback
    provider_type: openai
    api_key: "${OPENAI_API_KEY}"
    models: [gpt-4o]
    priority: 10

model_aliases:
  production-chat: gpt-4o
  stable-chat: production-chat

router:
  strategy: priority_based

Alias chains are validated and flattened at startup; empty values, cycles, canonical-name collisions, and targets without an enabled deployment fail startup. Alias names appear in /v1/models alongside canonical models. Lower numeric priority wins under priority_based; omitted provider priorities default to 0. When rolling back to a binary that predates these fields, remove model_aliases and priority from YAML before rolling back the binary, because unknown fields are rejected.

Core Subsystem Runtime Status

Runtime wiring decisions are tracked in src/core/subsystem_registry.rs, and tests assert that every module exported from src/core/mod.rs is either referenced by the gateway runtime or explicitly classified. The current issue-838 subsystem decisions are:

Installation

# Full gateway with SQLite + Redis (default)
[dependencies]
litellm-rs = "0.6"

# API-only - lightweight, no actix-web/argon2/aes-gcm/clap
[dependencies]
litellm-rs = { version = "0.6", default-features = false }

# API-only with metrics
[dependencies]
litellm-rs = { version = "0.6", default-features = false, features = ["lite"] }

# Gateway modules in library context (not standalone gateway binary runtime)
[dependencies]
litellm-rs = { version = "0.6", default-features = false, features = ["gateway"] }

Provider Support

Providers are organised into two tiers (see CLAUDE.md → Provider Tiers for the engineering definition).

• Tier 1 — catalog-only: OpenAI-compatible endpoints declared as data in src/core/providers/registry/catalog.rs. Routed through OpenAILikeProvider. Always available (no cargo feature required). The current crate runtime exposes chat completions and chat streaming for these providers; embeddings, images, audio, and other non-chat endpoints are not forwarded yet.
• Tier 2 — code-based: providers with custom request/response handling, auth signing, or streaming. Wired into the Provider enum and the factory. Some Tier 2 builders are feature-gated.

Router deployments use the closed Provider enum. Implementing LLMProvider alone does not make a third-party provider routeable; use the generic OpenAI-compatible path for compatible endpoints, or wire a code-based provider into the enum, dispatch, registry metadata, and factory.

The provider and legacy adapter matrices below are validated against the provider registry and Tier 1 catalog. The source of truth for Tier 1 entries is catalog.rs; Tier 2 identity and dispatch metadata lives in src/core/providers/registry/types.rs, with construction branches in src/core/providers/factory/registry.rs. Legacy adapter availability lives in src/core/providers/registry/support_matrix.rs. passthrough means a retained adapter forwards the call to the upstream OpenAI-compatible endpoint without per-provider transformation.

Legacy adapter matrix

This matrix records selector-based compatibility adapters. It is not the capability source for LLMClient::from_runtime, DefaultRouter::from_runtime, or the free completion() functions. Canonical runtime support is derived from the selected deployment's ProviderCapability; unsupported capabilities fail closed with a typed provider error.

The runway-media feature exposes a library-only Runway task contract for text/image-to-video submit, query, cancel, and result polling. It does not mount a gateway video route.

Tier 2 — code-based providers

Tier 1 — catalog providers (OpenAI-compatible, always available)

All entries below route through OpenAILikeProvider. Chat and streaming work for any endpoint that follows OpenAI's /chat/completions SSE protocol. Embeddings, images, audio, and other non-chat endpoints are not exposed through this path today, even when the upstream provider offers them.

Cloud (Bearer auth via env var):

groq, ai21, huggingface, baseten, together, together_ai, fireworks, fireworks_ai, perplexity, cerebras, openrouter, deepinfra, deepseek, novita, nvidia_nim, nebius, nscale, hyperbolic, featherless, galadriel, sambanova, heroku, friendliai, xai, moonshot, dashscope, qwen, baichuan, minimax, volcengine, xiaomi_mimo, zhipu, zai, lemonade, linkup, poe, wandb, nanogpt, aiml_api, aiml, aleph_alpha, anyscale, bytez, comet_api, compactifai, maritalk, siliconflow, yi, lambda_ai, ovhcloud

Local (no API key):

vllm, hosted_vllm, lm_studio, llamafile, docker_model_runner, xinference, infinity, oobabooga

Experimental / module-only

The following modules exist under src/core/providers/ (gated on providers-extra or providers-extended) but are not wired into the unified Provider enum or the factory today. They compile but cannot be selected through create_provider/from_config_async. Treat them as experimental scaffolding subject to change:

custom_api

For self-hosted or unlisted OpenAI-compatible endpoints, prefer the generic openai_compatible provider type instead.

Environment Variables

# Provider API Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=...
AZURE_OPENAI_API_KEY=...
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
AWS_REGION=us-east-1
GROQ_API_KEY=...
AI21_API_KEY=...
HF_TOKEN=...
BASETEN_API_KEY=...
DEEPSEEK_API_KEY=...
MOONSHOT_API_KEY=...
ZHIPU_API_KEY=...
MINIMAX_API_KEY=...

# Optional
LITELLM_VERBOSE=true  # Enable verbose logging

Examples

Multi-Provider Routing

use litellm_rs::{completion, user_message};

// Automatically routes to the right provider based on model name
let openai = completion("gpt-5.5", vec![user_message("Hi")], None).await?;
let anthropic = completion("anthropic/claude-opus-4-8", vec![user_message("Hi")], None).await?;
let groq = completion("groq/llama-3.1-8b-instant", vec![user_message("Hi")], None).await?;
let bedrock = completion(
    "bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
    vec![user_message("Hi")],
    None,
)
.await?;

bedrock/ uses the native AWS Bedrock provider. It signs requests with AWS SigV4 and preserves AWS execution model IDs such as us.*, global.*, region-prefixed IDs, and Bedrock ARNs. Use openai_compatible for Bedrock Access Gateway or other OpenAI-compatible proxies instead.

Embeddings

use litellm_rs::{embedding, embed_text};

// Single text
let embedding = embed_text("text-embedding-3-small", "Hello world").await?;

// Batch
let embeddings = embedding(
    "text-embedding-3-small",
    vec!["Hello", "World"],
    None,
).await?;

Streaming

use litellm_rs::{completion_stream, user_message};
use futures::StreamExt;

let mut stream = completion_stream(
    "gpt-4",
    vec![user_message("Tell me a story")],
    None,
).await?;

while let Some(chunk) = stream.next().await {
    if let Ok(chunk) = chunk {
        print!("{}", chunk.choices[0].delta.content.unwrap_or_default());
    }
}

Performance

Gateway throughput and latency claims require a dated raw artifact tied to an exact Git revision. See the reproducible gateway-overhead benchmark methodology for the fixed workload, commands, environment metadata, percentile reporting, and artifact format.

The Criterion benchmarks under benches/ measure in-process components; they must not be presented as end-to-end HTTP gateway throughput.

Troubleshooting

Build/test uses too much CPU or memory

• Use API-only defaults first: cargo test --lib --tests --no-default-features --features "lite"
• Limit local parallelism when needed: CARGO_BUILD_JOBS=4 cargo test --lib --tests --no-default-features --features "lite" -- --test-threads=4
• Avoid --all-features unless you are doing release/nightly validation

I only need provider API aggregation, not gateway

• Prefer default-features = false with features = ["lite"]
• Use gateway runtime commands only when you need HTTP server/auth/storage middleware

Documentation

• API Documentation
• Documentation Index
• Development Gateway Config
• Production Gateway Config
• Examples

Contributing

See CONTRIBUTING.md for development setup and guidelines.

Security

See SECURITY.md for security policy and vulnerability reporting.

The Agent Infra Stack

This project is one layer of an open-source stack for running coding agents (Claude Code, Codex) as serious infrastructure. Every piece works standalone; together they close the loop:

litellm-rs is the Route layer — the gateway underneath everything else, speaking OpenAI format to 100+ providers.

License

MIT License - see LICENSE for details.

Acknowledgments

Inspired by LiteLLM (Python).