LiteLLM: 22,500 Stars — 部署一个 API 调用 100+ LLM，内置故障转移 — 2026 生产级网关配置

# 创建项目目录
mkdir -p litellm-gateway && cd litellm-gateway

# 下载官方 docker-compose.yml
curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml

# 创建环境文件
cat > .env << 'EOF'
LITELLM_MASTER_KEY="sk-litellm-admin-$(openssl rand -hex 16)"
LITELLM_SALT_KEY="sk-salt-$(openssl rand -hex 32)"
OPENAI_API_KEY="sk-your-openai-key"
ANTHROPIC_API_KEY="sk-your-anthropic-key"
DATABASE_URL="postgresql://llmproxy:dbpassword9090@db:5432/litellm"
EOF

# litellm_config.yaml
model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY
      rpm: 500
      tpm: 150000

  - model_name: claude-sonnet
    litellm_params:
      model: anthropic/claude-sonnet-4-20250514
      api_key: os.environ/ANTHROPIC_API_KEY
      rpm: 200
      tpm: 40000

  - model_name: gemini-flash
    litellm_params:
      model: gemini/gemini-2.0-flash
      api_key: os.environ/GEMINI_API_KEY
      rpm: 1000

  - model_name: ollama-llama
    litellm_params:
      model: ollama/llama3.3
      api_base: http://ollama:11434
    model_info:
      mode: chat

  # 嵌入模型
  - model_name: text-embedding
    litellm_params:
      model: openai/text-embedding-3-small
      api_key: os.environ/OPENAI_API_KEY

general_settings:
  master_key: os.environ/LITELLM_MASTER_KEY
  database_url: os.environ/DATABASE_URL
  max_budget: 10000.00
  budget_duration: 30d
  alerting:
    - slack
  alerting_threshold: 300
  global_max_parallel_requests: 200

litellm_settings:
  drop_params: true
  num_retries: 3
  request_timeout: 120

  # 自动故障转移
  fallbacks:
    - gpt-4o:
      - claude-sonnet
      - gemini-flash
    - claude-sonnet:
      - gpt-4o
      - gemini-flash

  # Redis 缓存
  cache: true
  cache_params:
    type: redis
    host: redis
    port: 6379
    ttl: 3600

  # 可观测性回调
  success_callback: ["prometheus"]
  failure_callback: ["prometheus"]

# 拉取并启动所有服务
docker compose up -d

# 验证服务健康状态
docker compose ps

# 查看代理日志
docker compose logs -f litellm

# 测试对话补全
curl http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "什么是 LiteLLM？"}]
  }'

# 测试嵌入
curl http://localhost:4000/v1/embeddings \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding",
    "input": ["LiteLLM 是一个 AI 网关"]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000",
    api_key="sk-your-litellm-virtual-key"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "解释负载均衡"}]
)
print(response.choices[0].message.content)

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="claude-sonnet",
    openai_api_key="sk-your-virtual-key",
    openai_api_base="http://localhost:4000"
)

result = llm.invoke("LLM 网关有哪些类型？")
print(result.content)

from anthropic import Anthropic

client = Anthropic(
    base_url="http://localhost:4000/anthropic",
    api_key="sk-your-virtual-key"
)

response = client.messages.create(
    model="claude-sonnet",
    max_tokens=1024,
    messages=[{"role": "user", "content": "比较 LiteLLM 和 OpenRouter"}]
)
print(response.content[0].text)

# 添加到 litellm_config.yaml
model_list:
  - model_name: local-llama
    litellm_params:
      model: ollama/llama3.3
      api_base: http://localhost:11434
    model_info:
      mode: chat

# 通过 LiteLLM 测试本地模型
curl http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-llama",
    "messages": [{"role": "user", "content": "你好本地模型"}]
  }'

model_list:
  - model_name: cohere-command
    litellm_params:
      model: cohere/command-r-plus
      api_key: os.environ/COHERE_API_KEY

from openai import OpenAI
client = OpenAI(base_url="http://localhost:4000", api_key="sk-virtual-key")
response = client.chat.completions.create(
    model="cohere-command",
    messages=[{"role": "user", "content": "总结一下"}]
)

# 为 "前端团队" 创建虚拟密钥
curl -X POST http://localhost:4000/key/generate \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "key_alias": "frontend-team-key",
    "team_id": "frontend-team",
    "models": ["gpt-4o", "gemini-flash"],
    "max_budget": 500.00,
    "budget_duration": "30d",
    "rpm_limit": 100,
    "tpm_limit": 50000,
    "metadata": {
      "service": "customer-chat-widget",
      "env": "production"
    }
  }'

# 响应：
# {
#   "key": "sk-litellm-abc123...",
#   "expires": null,
#   "max_budget": 500.00,
#   "models": ["gpt-4o", "gemini-flash"]
# }

general_settings:
  provider_budget_config:
    openai:
      monthly_budget: 5000.00
    anthropic:
      monthly_budget: 3000.00
    gemini:
      monthly_budget: 1000.00

router_settings:
  routing_strategy: latency-based-routing
  routing_strategy_args:
    ttl: 60
  allowed_fails: 3
  cooldown_time: 60
  num_retries: 2
  timeout: 90
  retry_after: 5

# 安全加固版 config.yaml
general_settings:
  master_key: os.environ/LITELLM_MASTER_KEY
  database_url: os.environ/DATABASE_URL

  # 生产环境强制 HTTPS
  # 在 Nginx 或 AWS ALB 后部署，启用 TLS 终止

  # 禁用详细日志
  litellm_settings:
    set_verbose: false

  # 静态加密密钥
  litellm_settings:
    key_generation_algorithm: "rsa"
    allow_user_auth: false

# 添加 LiteLLM Helm 仓库
helm pull oci://docker.litellm.ai/berriai/litellm-helm

# 使用自定义值安装
helm install litellm-gateway ./litellm-helm \
  --namespace litellm \
  --create-namespace \
  --set replicaCount=3 \
  --set ingress.enabled=true \
  --set ingress.hosts[0].host=litellm.yourdomain.com \
  --set env.LITELLM_MASTER_KEY="sk-$(openssl rand -hex 16)" \
  --set env.DATABASE_URL="postgresql://user:pass@neon-host/litellm"

# 添加到 config.yaml
litellm_settings:
  success_callback: ["prometheus"]
  failure_callback: ["prometheus"]

# 按模型的请求速率
rate(litellm_request_total_requests[5m])

# 错误率
rate(litellm_requests_total_failed[5m])

# 每个密钥的剩余预算
litellm_remaining_requests

# 网关开销直方图
histogram_quantile(0.95, litellm_overhead_latency_ms_bucket)