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高性能AI网关
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高性能AI网关

基于 Rust · 开源免费,本地部署,数据完全自主可控
英文名:litellm-rs
⭐ 66 Stars 🍴 14 Forks 💻 Rust 📄 MIT 🏷 AI 8.0分
8.0AI 综合评分
ai-gatewayasync-rustapi-client
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:高性能AI网关 是一款优质的AI工具。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。

📚 深度解析

高性能AI网关 是一款基于 Rust 的开源工具,在 GitHub 上收获 0k+ Star,是ai-gateway、async-rust、api-client领域中的优质开源项目。开源工具的最大优势在于代码完全透明,你可以审计每一行代码的安全性,也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS?**
对于个人开发者和有隐私需求的用户,本地部署的开源工具意味着数据不离本机,不受第三方服务商的数据政策约束。同时,开源工具通常没有使用次数限制和月度费用,一次安装即可长期使用,对于高频使用场景的总拥有成本(TCO)远低于订阅制商业工具。

**安装与环境准备**
高性能AI网关 依赖 Rust 运行环境。建议通过 pyenv(Python)或 nvm(Node.js)管理 Rust 版本,避免全局环境污染。对于新手用户,推荐先创建虚拟环境(python -m venv venv && source venv/bin/activate),再安装依赖,这样即使出现问题也可以随时删除虚拟环境重新开始,不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues(已关闭的问题),大多数常见问题都已有解答。遇到 Bug 时,提供 pip list 的输出、完整错误堆栈和最小可复现示例,能显著提高开发者响应速度。AI Skill Hub 将持续追踪 高性能AI网关 的版本更新,及时通知重要功能变化。

📋 工具概览

高性能AI网关,支持100+LLM API调用

高性能AI网关 是一款基于 Rust 开发的开源工具,专注于 ai-gateway、async-rust、api-client 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

GitHub Stars
⭐ 66
开发语言
Rust
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
8.0 分
工具类型
AI工具
Forks
14

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

高性能AI网关,支持100+LLM API调用

高性能AI网关 是一款基于 Rust 开发的开源工具,专注于 ai-gateway、async-rust、api-client 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

📌 核心特色
  • 开源免费,支持本地部署,数据完全自主可控
  • 活跃的 GitHub 开源社区,持续迭代更新
  • 提供详细文档和使用示例,新手友好
  • 支持自定义配置,灵活适配不同使用环境
  • 可作为基础组件集成进现有技术栈或进行二次开发
🎯 主要使用场景
  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:cargo install(推荐)
cargo install litellm-rs

# 方式二:从源码编译
git clone https://github.com/majiayu000/litellm-rs
cd litellm-rs
cargo build --release
# 二进制在 ./target/release/litellm-rs
📋 安装步骤说明
  1. 访问 GitHub 仓库页面
  2. 按照 README 文档完成依赖安装
  3. 根据系统环境完成初始化配置
  4. 参考官方示例或文档开始使用
  5. 遇到问题可在 GitHub Issues 中查找解答
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 查看帮助
litellm-rs --help

# 基本运行
litellm-rs [options] <input>

# 详细使用说明请查阅文档
# https://github.com/majiayu000/litellm-rs
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# litellm-rs 配置说明
# 查看配置选项
litellm-rs --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量(覆盖配置文件)
export LITELLM_RS_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 76/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

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.

Crates.io Documentation License: MIT

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

Installation

Since 0.8.0, default features select the reusable library with metrics and tracing. Gateway and database dependencies are opt-in. The previous 0.7.0 release used gateway defaults; gateway consumers must explicitly select a storage feature such as sqlite or postgres.

```toml

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

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 --features sqlite

Or install the gateway binary:

cargo install litellm-rs --version 0.8.2 --locked --bin gateway --no-default-features \
  --features postgres,sqlite,redis,s3,metrics,tracing,websockets,providers-extra,providers-extended,mcp-validation,a2a
mkdir -p config
curl -L https://raw.githubusercontent.com/majiayu000/litellm-rs/v0.8.2/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. The explicit sqlite feature enables the storage needed by the gateway binary. The registry install selects the same feature profile as release archives and containers, including MCP, A2A and Realtime. Binary and example config versions must match. See the 0.8 artifact verification for source and published-artifact verification.

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.

API-key budgets currently use process-local definitions and counters. Persisting a key's budget_id does not restore its budget after restart or share its balance with another gateway; an unavailable binding fails closed. The key management API rejects max_budget. Redis-backed provider/model budgets are a separate scope; see the budget capability boundary.

For a first HTTP request or setup failure, use the gateway setup and compatibility questions.

Library Example

Add Tokio with macros and rt-multi-thread features. The application must install its configured RuntimeBinding with install_default_runtime before calling completion; the verified consumer example shows runtime setup and successful calls against a local mock.

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(())
}

Examples

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:

SubsystemDecisionRuntime status
core/guardrailswireDefault-on prompt-injection checks run before provider execution and on non-streaming output; guardrails.enabled: false is the explicit opt-out.
core/ip_accesswireConfigured allow/block rules run as an outer Actix middleware and short-circuit before downstream side effects; empty/default rules allow all.
core/mcpfeature-gatedgateway,mcp mounts authenticated Streamable HTTP at /{server_name}/mcp (/mcp for one enabled server). See [MCP gateway](docs/gateway/mcp.md) for configuration and stateless transport limits. Responses API MCP descriptors pass through independently.
core/a2aexperimental-gateOpt-in gateway,a2a exposes authenticated A2A 1.0 agent cards, messages, task queries/cancellation and SSE. Task ownership is process-local; see [A2A gateway](docs/gateway/a2a.md).
server/routes/ai/realtimefeature-gatedgateway,websockets mounts the [manual OpenAI Realtime gateway](docs/gateway/realtime.md). The unused legacy core/realtime client has been removed.
core/observability and core/integrationswireConfigured Langfuse, OpenTelemetry, and Datadog backends are initialized at startup and receive real chat, completion, response, and embedding lifecycle events.
core/auditwireenterprise.audit_logging: true registers request audit middleware; events use structured JSON on stderr unless a file or custom output is configured. Default is off.
core/batchlibrary-only/v1/batches remains a wired provider proxy. Domain records and async batch helpers remain; the unreachable BatchProcessor has been removed.
Former core/webhooksremovedThe unused library and webhooks feature have been removed in 0.8.0. Budget-alert delivery and provider-native webhook fields remain separate.
Former core/semantic_cache and core/analyticsremovedRemoved in 0.8.0 after the expired 0.7 deadline. Deterministic caching, request metrics and callbacks remain; see the migration guide for removed fields/features.
core/virtual_keyswireRuntime virtual keys use the canonical core::keys::KeyManager; the duplicate legacy manager has been removed. Storage record types remain in use.
core/user_managementinternalDomain records back current auth/storage paths. The unused UserManager and its user-management feature have been removed.

Library without optional observability

[dependencies] litellm-rs = { version = "0.8", default-features = false }

Environment Variables

```bash

Optional

LITELLM_VERBOSE=true # Enable verbose logging ```

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=...

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

Troubleshooting

🇨🇳 中文文档镜像 AI 翻译 2026-06-06
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

litellm-rs 是一个高性能的 Rust 库和网关,用于在 OpenAI 兼容格式下调用 LLM API。它内置了 50+ 个 OpenAI 兼容提供商,首选适配器包括 OpenAI、Anthropic、AWS Bedrock、Mistral 和 Cloudflare。

⚡ 功能介绍

litellm-rs 的功能包括:60+ 个 runtime-wired 提供商,OpenAI 兼容 API,高性能,智能路由等。

🛠 安装步骤(Docker/pip/源码)

安装 litellm-rs 可以使用 Cargo,具体步骤如下:

🚀 使用教程

使用 litellm-rs 的快速入门(5 分钟,API-Only 推荐):

⚙️ 配置说明(含 MCP / env)

环境变量配置:

🔌 API 说明

作为库使用 litellm-rs 的 API 文档:

🔄 工作流/模块

工作流和模块说明:

❓ FAQ 摘要

常见问题解答:

🎯 aiskill88 AI 点评 A 级 2026-06-06

高性能AI网关,支持多个LLM API调用,值得关注

📚 实用指南(长尾问题)
适合谁
  • 构建企业知识库 / RAG 检索应用的团队
最佳实践
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
部署方案
  • CLI:直接 npm install -g / pip install,命令行调用
  • 本地部署:CPU 8GB 起,GPU 推荐 16GB+ 显存
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
litellm-rs 中文教程litellm-rs 安装报错怎么办litellm-rs 与同类工具对比litellm-rs 最佳实践litellm-rs 适合谁用

⚡ 核心功能

👥 适合谁
  • 构建企业知识库 / RAG 检索应用的团队
⭐ 最佳实践
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型

👥 适合人群

AI 技术爱好者研究人员和学生开发者和工程师技术创业者

🎯 使用场景

  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +完全开源免费,无授权费用
  • +本地部署,数据完全自主可控
  • +开发者社区支持,遇问题可查可问
⚠️ 不足
  • −安装和初始配置可能需要一定技术基础
  • −功能完整性通常不如成熟商业产品
  • −技术支持主要依赖开源社区,响应速度不稳定
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

litellm-rs 是一款Rust开发的AI辅助工具。开源AI工具:A high-performance AI Gateway written in Rust — call 100+ LLM APIs using OpenAI 。⭐66 · Rust 主要应用场景包括:快速调用多个LLM API。
💡 AI Skill Hub 点评

总体来看,高性能AI网关 是一款质量优秀的AI工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

📚 深入学习 高性能AI网关
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 litellm-rs
原始描述 开源AI工具:A high-performance AI Gateway written in Rust — call 100+ LLM APIs using OpenAI 。⭐66 · Rust
Topics ai-gatewayasync-rustapi-client
GitHub https://github.com/majiayu000/litellm-rs
License MIT
语言 Rust
🔗 原始来源
🐙 GitHub 仓库  https://github.com/majiayu000/litellm-rs

收录时间:2026-06-06 · 更新时间:2026-06-06 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

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