LiteLLM AI Gateway

面向 100+ LLM 的开源 AI Gateway。可自托管。企业级就绪。以 OpenAI 格式调用任意 LLM。

---

LiteLLM 是一个开源 AI Gateway，通过 OpenAI 格式为你提供单一统一接口，可调用 100 多家 LLM 提供商，包括 OpenAI、Anthropic、Gemini、Bedrock、Azure 等。

你可以将其作为 Python SDK，直接集成到代码库中；也可以部署 AI Gateway（代理服务器），将其作为团队或组织的集中式服务。

跳转至 LiteLLM Proxy（LLM Gateway）文档
跳转至支持的 LLM 提供商

---

跨提供商管理 LLM 调用很快就会变得复杂：每种模型都有不同的 SDK、认证模式、请求格式和错误类型。LiteLLM 消除了这些摩擦：

• 统一 API —— 一个接口对接 100 多种 LLM，无需在各提供商 SDK 之间周旋
• 可直接替换的 OpenAI 兼容性 —— 无需重写代码即可切换提供商
• 生产级网关 —— 开箱即用地提供虚拟密钥、支出追踪、Guardrails、负载均衡和管理仪表盘
• 在 1,000 RPS 下实现 8ms P95 延迟（基准测试）

---

LLM - 调用 100+ LLM（Python SDK + AI Gateway）

所有支持的端点 - /chat/completions、/responses、/embeddings、/images、/audio、/batches、/rerank、/a2a、/messages 等。

独立的 litellm-core 发行包提供与 import litellm 相同 API 和运行时依赖的 Python SDK。它不包含可选扩展、CLI 入口点或内置仪表盘。由于 litellm 和 litellm-core 拥有重叠的 Python 文件，每个环境只能安装一个 SDK 发行包。若需要代理、CLI 和可选扩展，请使用 litellm。

在发布集成完成前，可从当前检出目录构建并安装 core：

python scripts/build_core_distribution.py --out-dir dist/core
python -m pip install dist/core/litellm_core-*.whl

构建器需要 Git、uv 和 Rust 构建工具链。它从根目录的 pyproject.toml 读取发布版本，不会修改源文件，并在 dist/core 中生成 wheel 和自包含的 sdist。请在未安装 litellm 的全新环境中执行安装。

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

# OpenAI
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])

# Anthropic  
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}])

AI Gateway（代理服务器）

快速入门：端到端教程 - 设置虚拟密钥并发起首个请求

uv tool install 'litellm[proxy]'
litellm --model gpt-4o


import openai

client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

文档：LLM 提供商

Agent - 调用 A2A Agent（Python SDK + AI Gateway）

支持的提供商 - LangGraph、Vertex AI Agent Engine、Azure AI Foundry、Bedrock AgentCore、Pydantic AI

Python SDK - A2A 协议

from litellm.a2a_protocol import A2AClient
from a2a.types import SendMessageRequest, MessageSendParams
from uuid import uuid4

client = A2AClient(base_url="http://localhost:10001")

request = SendMessageRequest(
    id=str(uuid4()),
    params=MessageSendParams(
        message={
            "role": "user",
            "parts": [{"kind": "text", "text": "Hello!"}],
            "messageId": uuid4().hex,
        }
    )
)
response = await client.send_message(request)

AI Gateway（代理服务器）

第 1 步。 将你的 Agent 添加至 AI Gateway —— 根据 Agent 设置 protocolVersion 为 1.0 或 0.3

第 2 步。 通过 A2A SDK 调用 Agent（要求 a2a-sdk>=1.1.0）

import httpx
from a2a.client import A2ACardResolver, ClientConfig, ClientFactory
from a2a.types import Message, Part, Role, SendMessageRequest
from a2a.utils.constants import TransportProtocol
from uuid import uuid4

base_url = "http://localhost:4000/a2a/my-agent"  # LiteLLM proxy + agent name
headers = {"Authorization": "Bearer <your-master-key>"}    # LiteLLM master key or a virtual key

async with httpx.AsyncClient(headers=headers, timeout=60.0) as http_client:
    resolver = A2ACardResolver(httpx_client=http_client, base_url=base_url)
    agent_card = await resolver.get_agent_card()
    config = ClientConfig(
        httpx_client=http_client,
        streaming=False,
        supported_protocol_bindings=[TransportProtocol.JSONRPC, TransportProtocol.HTTP_JSON],
    )
    client = ClientFactory(config).create(agent_card)

    request = SendMessageRequest(
        message=Message(
            message_id=uuid4().hex,
            role=Role.ROLE_USER,
            parts=[Part(text="Hello!")],
        )
    )
    async for event in client.send_message(request):
        populated = event.ListFields()
        if populated and populated[0][0].name in ("message", "msg"):
            print("".join(getattr(p, "text", "") or "" for p in populated[0][1].parts))

文档：A2A Agent Gateway

MCP 工具 - 将 MCP 服务器连接至任意 LLM（Python SDK + AI Gateway）

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from litellm import experimental_mcp_client
import litellm

server_params = StdioServerParameters(command="python", args=["mcp_server.py"])

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()

        # Load MCP tools in OpenAI format
        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")

        # Use with any LiteLLM model
        response = await litellm.acompletion(
            model="gpt-4o",
            messages=[{"role": "user", "content": "What's 3 + 5?"}],
            tools=tools
        )

第 1 步。 将 MCP Server 添加至 AI Gateway

第 2 步。 通过 /chat/completions 调用 MCP 工具

curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
  -H 'Authorization: Bearer <your-master-key>' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Summarize the latest open PR"}],
    "tools": [{
      "type": "mcp",
      "server_url": "litellm_proxy/mcp/github",
      "server_label": "github_mcp",
      "require_approval": "never"
    }]
  }'


{
  "mcpServers": {
    "LiteLLM": {
      "url": "http://localhost:4000/mcp/",
      "headers": {
        "x-litellm-api-key": "Bearer <your-master-key>"
      }
    }
  }
}

对于 MCP OAuth，上游服务可能会宣告支持动态客户端注册，但以 HTTP 401 或 403 拒绝请求。如果提供商要求使用预注册的 OAuth 应用，请在 MCP Server 上配置其 credentials.client_id，并在需要时配置 credentials.client_secret。这会在网关登录流程中跳过动态注册。提供商必须批准该应用访问 MCP；仅进入其授权页面，并不意味着登录或工具调用一定会成功。

文档：MCP Gateway

Agent - 在任意模型上运行 Claude Code、Codex、OpenCode 或 Deep Agents（Python SDK）

import litellm
from litellm import Harness, sandbox

result = litellm.agent(
    Harness.CLAUDE_CODE,  # or Harness.CODEX, Harness.OPENCODE, Harness.DEEPAGENTS
    "Find why tests/test_router.py is flaky and fix it.",
    sandbox=sandbox.local("./repo"),
    model="litellm_proxy/claude-sonnet-4-5",  # a model group on your AI Gateway
)

print(result.text, result.cost, [f.path for f in result.files])

设置 LITELLM_PROXY_API_BASE 和 LITELLM_PROXY_API_KEY 后，Agent 发起的每次模型调用都会通过你的 AI Gateway，并被标记为 harness,claude_code。移除 litellm_proxy/ 前缀即可直接调用提供商。请安装 starlette uvicorn 以及 Agent 的 CLI（claude、codex 或 opencode）；对于 Deep Agents，则安装 deepagents langchain-litellm。

文档：Agent Harnesses

阅读文档

---

你可以通过 Proxy Server 或 Python SDK 使用 LiteLLM。两者都提供访问多个 LLM（100+ LLM）的统一接口。请选择最适合自身需求的方案：

| LiteLLM AI Gateway | LiteLLM Python SDK
---|---|---
使用场景 | 用于访问多个 LLM 的集中式服务（LLM Gateway） | 在 Python 代码中直接使用 LiteLLM
适用人群 | 生成式 AI 赋能团队 / ML 平台团队 | 构建 LLM 项目的开发者
核心功能 | 集中式 API Gateway，具备身份验证与授权；按项目/用户进行多租户成本追踪和支出管理；按项目定制日志、Guardrails、缓存；使用虚拟密钥实现安全访问控制；通过管理仪表盘 UI 进行监控与管理 | 直接将 Python 库集成到代码库；Router 可在多个部署之间执行重试/回退逻辑（例如 Azure/OpenAI）——Router；应用层负载均衡和成本追踪；提供 OpenAI 兼容错误的异常处理；可观测性回调（Lunary、MLflow、Langfuse 等）

稳定版： 请使用带有 -stable 标签的 Docker 镜像。这些镜像会先经过 12 小时负载测试，再行发布。在此了解更多发布周期信息

支持更多提供商。如缺少某个提供商或 LLM Platform，请提交功能请求。

使用 Terraform 部署到 AWS 或 GCP

使用已发布的 Terraform 模块，将 LiteLLM proxy 作为生产级组件化堆栈运行（Gateway、后端和 UI 分别部署为独立服务；使用托管 Postgres、Redis 和对象存储）。两个模块均位于公开 Terraform Registry，无需认证。

AWS —— ECS Fargate + Aurora + ElastiCache + ALB

[![Launch in AWS Clou

LiteLLM AI Gateway

Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.

---

LiteLLM is an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure, and more — using the OpenAI format.

Use it as a Python SDK for direct library integration, or deploy the AI Gateway (Proxy Server) as a centralized service for your team or organization.

Jump to LiteLLM Proxy (LLM Gateway) Docs
Jump to Supported LLM Providers

---

Managing LLM calls across providers gets complicated fast — different SDKs, auth patterns, request formats, and error types for every model. LiteLLM removes that friction:

• Unified API — one interface for 100+ LLMs, no provider-specific SDK juggling
• Drop-in OpenAI compatibility — swap providers without rewriting your code
• Production-ready gateway — virtual keys, spend tracking, guardrails, load balancing, and an admin dashboard out of the box
• 8ms P95 latency at 1k RPS (benchmarks)

---

LLMs - Call 100+ LLMs (Python SDK + AI Gateway)

All Supported Endpoints - /chat/completions, /responses, /embeddings, /images, /audio, /batches, /rerank, /a2a, /messages and more.

An independent litellm-core distribution provides the Python SDK with the same import litellm API and runtime dependencies. It has no optional extras, CLI entry points, or bundled dashboard. Install one SDK distribution per environment because litellm and litellm-core own overlapping Python files. Use litellm for the proxy, CLI, and optional extras

Build and install core from this checkout while its release integration is pending:

python scripts/build_core_distribution.py --out-dir dist/core
python -m pip install dist/core/litellm_core-*.whl

The builder requires Git, uv and the Rust build toolchain. It reads the release version from the root pyproject.toml, leaves source files unchanged, and produces a wheel and a self-contained sdist in dist/core. Run installation in a fresh environment without litellm

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

# OpenAI
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])

# Anthropic  
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}])

AI Gateway (Proxy Server)

Getting Started - E2E Tutorial - Setup virtual keys, make your first request

uv tool install 'litellm[proxy]'
litellm --model gpt-4o


import openai

client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Docs: LLM Providers

Agents - Invoke A2A Agents (Python SDK + AI Gateway)

Supported Providers - LangGraph, Vertex AI Agent Engine, Azure AI Foundry, Bedrock AgentCore, Pydantic AI

Python SDK - A2A Protocol

from litellm.a2a_protocol import A2AClient
from a2a.types import SendMessageRequest, MessageSendParams
from uuid import uuid4

client = A2AClient(base_url="http://localhost:10001")

request = SendMessageRequest(
    id=str(uuid4()),
    params=MessageSendParams(
        message={
            "role": "user",
            "parts": [{"kind": "text", "text": "Hello!"}],
            "messageId": uuid4().hex,
        }
    )
)
response = await client.send_message(request)

AI Gateway (Proxy Server)

Step 1. Add your Agent to the AI Gateway — set protocolVersion to 1.0 or 0.3 per agent

Step 2. Call Agent via A2A SDK (requires a2a-sdk>=1.1.0)

import httpx
from a2a.client import A2ACardResolver, ClientConfig, ClientFactory
from a2a.types import Message, Part, Role, SendMessageRequest
from a2a.utils.constants import TransportProtocol
from uuid import uuid4

base_url = "http://localhost:4000/a2a/my-agent"  # LiteLLM proxy + agent name
headers = {"Authorization": "Bearer <your-master-key>"}    # LiteLLM master key or a virtual key

async with httpx.AsyncClient(headers=headers, timeout=60.0) as http_client:
    resolver = A2ACardResolver(httpx_client=http_client, base_url=base_url)
    agent_card = await resolver.get_agent_card()
    config = ClientConfig(
        httpx_client=http_client,
        streaming=False,
        supported_protocol_bindings=[TransportProtocol.JSONRPC, TransportProtocol.HTTP_JSON],
    )
    client = ClientFactory(config).create(agent_card)

    request = SendMessageRequest(
        message=Message(
            message_id=uuid4().hex,
            role=Role.ROLE_USER,
            parts=[Part(text="Hello!")],
        )
    )
    async for event in client.send_message(request):
        populated = event.ListFields()
        if populated and populated[0][0].name in ("message", "msg"):
            print("".join(getattr(p, "text", "") or "" for p in populated[0][1].parts))

Docs: A2A Agent Gateway

MCP Tools - Connect MCP servers to any LLM (Python SDK + AI Gateway)

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from litellm import experimental_mcp_client
import litellm

server_params = StdioServerParameters(command="python", args=["mcp_server.py"])

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()

        # Load MCP tools in OpenAI format
        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")

        # Use with any LiteLLM model
        response = await litellm.acompletion(
            model="gpt-4o",
            messages=[{"role": "user", "content": "What's 3 + 5?"}],
            tools=tools
        )

Step 1. Add your MCP Server to the AI Gateway

Step 2. Call MCP tools via /chat/completions

curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
  -H 'Authorization: Bearer <your-master-key>' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Summarize the latest open PR"}],
    "tools": [{
      "type": "mcp",
      "server_url": "litellm_proxy/mcp/github",
      "server_label": "github_mcp",
      "require_approval": "never"
    }]
  }'


{
  "mcpServers": {
    "LiteLLM": {
      "url": "http://localhost:4000/mcp/",
      "headers": {
        "x-litellm-api-key": "Bearer <your-master-key>"
      }
    }
  }
}

For MCP OAuth, an upstream may advertise dynamic client registration but refuse requests with HTTP 401 or 403. If the provider requires a pre-registered OAuth app, configure its credentials.client_id and, when required, credentials.client_secret on the MCP server. This skips dynamic registration in the gateway sign-in flow. The provider must approve the app for MCP access; reaching its authorization page does not establish that login or tool calls will succeed

Docs: MCP Gateway

Agents - Run Claude Code, Codex, OpenCode or Deep Agents on any model (Python SDK)

import litellm
from litellm import Harness, sandbox

result = litellm.agent(
    Harness.CLAUDE_CODE,  # or Harness.CODEX, Harness.OPENCODE, Harness.DEEPAGENTS
    "Find why tests/test_router.py is flaky and fix it.",
    sandbox=sandbox.local("./repo"),
    model="litellm_proxy/claude-sonnet-4-5",  # a model group on your AI Gateway
)

print(result.text, result.cost, [f.path for f in result.files])

Set LITELLM_PROXY_API_BASE and LITELLM_PROXY_API_KEY and every model call the agent makes goes through your AI Gateway, tagged harness,claude_code. Drop the litellm_proxy/ prefix to call a provider directly. Install starlette uvicorn plus the agent's CLI (claude, codex or opencode), or deepagents langchain-litellm for Deep Agents.

Docs: Agent Harnesses

Read the Docs

---

You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs:

| LiteLLM AI Gateway | LiteLLM Python SDK
---|---|---
Use Case | Central service (LLM Gateway) to access multiple LLMs | Use LiteLLM directly in your Python code
Who Uses It? | Gen AI Enablement / ML Platform Teams | Developers building LLM projects
Key Features | Centralized API gateway with authentication and authorization, multi-tenant cost tracking and spend management per project/user, per-project customization (logging, guardrails, caching), virtual keys for secure access control, admin dashboard UI for monitoring and management | Direct Python library integration in your codebase, Router with retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router, application-level load balancing and cost tracking, exception handling with OpenAI-compatible errors, observability callbacks (Lunary, MLflow, Langfuse, etc.)

Stable Release: Use docker images with the -stable tag. These have undergone 12 hour load tests, before being published. More information about the release cycle here

Support for more providers. Missing a provider or LLM Platform, raise a feature request.

Deploy on AWS or GCP with Terraform

Run the LiteLLM proxy as a production-ready componentized stack (gateway, backend, UI on separate services; managed Postgres + Redis + object store) using the published Terraform modules. Both modules are on the public Terraform Registry — no auth needed.

AWS — ECS Fargate + Aurora + ElastiCache + ALB

— opens an in-browser shell, already authenticated to your AWS account. Once inside, run:

git clone https://github.com/BerriAI/litellm.git
cd litellm/terraform/litellm/aws/examples/default
cp terraform.tfvars.example terraform.tfvars   # edit region/tenant/env
terraform init && terraform apply

Module page →

Or call the module from your own root config:

# main.tf
terraform {
  required_version = ">= 1.6.0"
  required_providers {
    aws = { source = "hashicorp/aws", version = "~> 5.60" }
  }
}

provider "aws" {
  region = "us-west-2"
}

module "litellm" {
  source  = "BerriAI/litellm/aws"
  version = "~> 1.89"

  region = "us-west-2"
  azs    = ["us-west-2a", "us-west-2b"]
  tenant = "acme"
  env    = "prod"

  # Production: provide an ACM cert. Without one, set allow_plaintext_alb = true
  # (dev/trial only).
  # acm_certificate_arn = "arn:aws:acm:us-west-2:111122223333:certificate/..."
  allow_plaintext_alb = true
}

output "litellm_url" {
  value = module.litellm.alb_dns_name
}


terraform init
terraform apply

Provider API keys live in AWS Secrets Manager; reference ARNs via gateway_extra_secrets. Full input list and architecture diagram on the registry page.

GCP — Cloud Run + Cloud SQL + Memorystore + HTTPS LB

Real 1-click. Opens Cloud Shell, clones this repo, and walks you through terraform apply via a built-in DeployStack tutorial — pick the project, the tutorial sets up the Artifact Registry remote repo, writes terraform.tfvars from your answers, and runs apply.

Module page →

To call the module from your own config instead, Cloud Run can't pull from ghcr.io directly, so first set up a one-time Artifact Registry remote repo backed by GHCR:

gcloud artifacts repositories create litellm \
  --location=us-central1 \
  --repository-format=docker \
  --mode=remote-repository \
  --remote-docker-repo=https://ghcr.io \
  --project=my-gcp-project

Then:

# main.tf
terraform {
  required_version = ">= 1.6.0"
  required_providers {
    google      = { source = "hashicorp/google",      version = "~> 6.10" }
    google-beta = { source = "hashicorp/google-beta", version = "~> 6.10" }
  }
}

provider "google"      { project = "my-gcp-project"; region = "us-central1" }
provider "google-beta" { project = "my-gcp-project"; region = "us-central1" }

module "litellm" {
  source  = "BerriAI/litellm/google"
  version = "~> 1.89"

  project_id = "my-gcp-project"
  region     = "us-central1"
  tenant     = "acme"
  env        = "prod"

  # Replace my-gcp-project with your GCP project ID (same value as project_id above).
  image_registry = "us-central1-docker.pkg.dev/my-gcp-project/litellm/berriai"

  # Production: provide DNS already pointing at the LB IP for Google-managed certs.
  # Without one, set allow_plaintext_lb = true (dev/trial only).
  # lb_domains         = ["proxy.example.com"]
  allow_plaintext_lb = true
}

output "litellm_url" {
  value = module.litellm.load_balancer_url
}


terraform init
terraform apply

Provider API keys live in Secret Manager; reference resource IDs (e.g. projects/my-gcp-project/secrets/openai-api-key) via gateway_extra_secrets. Full input list and architecture diagram on the registry page.

• The full componentized split (gateway / backend / UI as independent services)
• Managed Postgres (writer + reader) and Redis
• Versioned object store for proxy state + file uploads
• An auto-generated LITELLM_MASTER_KEY in your cloud's secret manager
• A one-off migration job that runs prisma migrate deploy before the proxy starts
• The same proxy_config surface as the Helm chart — pass YAML as a typed map

The Terraform modules live at terraform/litellm/aws/ and terraform/litellm/gcp/ in this repo; the registry entries are read-only mirrors updated on each release.

1. Setup .env file in root

2. Run dependent services docker-compose up db prometheus

3. Run make bootstrap

4. Start proxy backend: uv run python litellm/proxy/proxy_cli.py

5. Navigate to ui/litellm-dashboard (dependencies were already installed w/ make bootstrap)

6. Start dashboard: npm run dev

Verify Docker Image Signatures

All LiteLLM Docker images published to GHCR are signed with cosign. Every release is signed with the same key introduced in commit 0112e53.

Verify using the pinned commit hash (recommended):

A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:

cosign verify \
  --key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
  ghcr.io/berriai/litellm:<release-tag>

Verify using a release tag (convenience):

Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:

cosign verify \
  --key https://raw.githubusercontent.com/BerriAI/litellm/<release-tag>/cosign.pub \
  ghcr.io/berriai/litellm:<release-tag>

Replace <release-tag> with the version you are deploying (e.g. v1.83.0-stable).

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For companies that need better security, user management and professional support

Get an Enterprise License Talk to founders

This covers:

• ✅ Features under theLiteLLM Commercial License:
• ✅ Feature Prioritization
• ✅ Custom Integrations
• ✅ Professional Support - Dedicated discord + slack
• ✅ Custom SLAs
• ✅ Secure access with Single Sign-On

We welcome contributions to LiteLLM! Whether you're fixing bugs, adding features, or improving documentation, we appreciate your help.

Quick Start for Contributors

This requires uv to be installed.

git clone https://github.com/BerriAI/litellm.git
cd litellm
make install-dev    # Install development dependencies
make format         # Format your code
make lint           # Run all linting checks
make test-unit      # Run unit tests
make format-check   # Check formatting only

For detailed contributing guidelines, see CONTRIBUTING.md.

📖 Contributing to documentation? The LiteLLM docs have moved to a separate repository: BerriAI/litellm-docs. Please open doc PRs there. Docs are served at docs.litellm.ai.

LiteLLM follows the Google Python Style Guide.

Our automated checks include:

• Ruff for formatting, linting, and code quality
• basedpyright for type checking
• Circular import detection
• Import safety checks

All these checks must pass before your PR can be merged.

Support / talk with founders