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<h1 align="center"> 🚅 LiteLLM </h1> <p align="center"> <p align="center">LiteLLM AI Gateway </p> <p align="center">Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.</p> <p align="center"> <a href="https://render.com/deploy?repo=https://github.com/BerriAI/litellm" target="_blank" rel="nofollow"><img src="https://render.com/images/deploy-to-render-bu

Claude Code Knowledge Pack7/10/2026

Overview

<h1 align="center"> 🚅 LiteLLM </h1> <p align="center"> <p align="center">LiteLLM AI Gateway </p> <p align="center">Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.</p> <p align="center"> <a href="https://render.com/deploy?repo=https://github.com/BerriAI/litellm" target="_blank" rel="nofollow"><img src="https://render.com/images/deploy-to-render-button.svg" alt="Deploy to Render"></a> <a href="https://railway.com/deploy/RhvhdC?referralCode=7mRv9K&utm_medium=integration&utm_source=template&utm_campaign=generic"> <img src="https://railway.com/button.svg" alt="Deploy on Railway"> </a> </p> </p> <h4 align="center"><a href="https://docs.litellm.ai/docs/simple_proxy" target="_blank">LiteLLM Proxy Server (AI Gateway)</a> | <a href="https://docs.litellm.ai/docs/enterprise#hosted-litellm-proxy" target="_blank"> Hosted Proxy</a> | <a href="https://litellm.ai/enterprise"target="_blank">Enterprise Tier</a> | <a href="https://www.litellm.ai/ai-gateway" target="_blank">Website</a></h4> <h4 align="center"> <a href="https://pypi.org/project/litellm/" target="_blank"> <img src="https://img.shields.io/pypi/v/litellm.svg" alt="PyPI Version"> </a> <a href="https://github.com/BerriAI/litellm" target="_blank"> <img src="https://img.shields.io/github/stars/BerriAI/litellm.svg?style=social" alt="GitHub Stars"> </a> <a href="https://www.ycombinator.com/companies/berriai"> <img src="https://img.shields.io/badge/Y%20Combinator-W23-orange?style=flat-square" alt="Y Combinator W23"> </a> <a href="https://wa.link/huol9n"> <img src="https://img.shields.io/static/v1?label=Chat%20on&message=WhatsApp&color=success&logo=WhatsApp&style=flat-square" alt="Whatsapp"> </a> <a href="https://discord.gg/wuPM9dRgDw"> <img src="https://img.shields.io/static/v1?label=Chat%20on&message=Discord&color=blue&logo=Discord&style=flat-square" alt="Discord"> </a> <a href="https://www.litellm.ai/support"> <img src="https://img.shields.io/static/v1?label=Chat%20on&message=Slack&color=black&logo=Slack&style=flat-square" alt="Slack"> </a> <a href="https://codspeed.io/BerriAI/litellm?utm_source=badge"> <img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed"/> </a> </h4> <img width="2688" height="1600" alt="Group 7154 (1)" src="https://github.com/user-attachments/assets/c5ee0412-6fb5-4fb6-ab5b-bafae4209ca6" />

What is LiteLLM

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 <br> Jump to Supported LLM Providers


Why LiteLLM

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)

OSS Adopters

<table> <tr> <td><img height="60" alt="Stripe" src="https://github.com/user-attachments/assets/f7296d4f-9fbd-460d-9d05-e4df31697c4b" /></td> <td><img height="60" alt="image" src="https://github.com/user-attachments/assets/436fca71-988b-40bb-b5fe-8450c80fdbd0" /></td> <td><img height="60" alt="Google ADK" src="https://github.com/user-attachments/assets/caf270a2-5aee-45c4-8222-41a2070c4f19" /></td> <td><img height="60" alt="Greptile" src="https://github.com/user-attachments/assets/0be4bd8a-7cfa-48d3-9090-f415fe948280" /></td> <td><img height="60" alt="OpenHands" src="https://github.com/user-attachments/assets/a6150c4c-149e-4cae-888b-8b92be6e003f" /></td> <td><h2>Netflix</h2></td> <td><img height="60" alt="OpenAI Agents SDK" src="https://github.com/user-attachments/assets/c02f7be0-8c2e-4d27-aea7-7c024bfaebc0" /></td> </tr> </table>

Features

<details open> <summary><b>LLMs</b> - Call 100+ LLMs (Python SDK + AI Gateway)</summary>

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

Python SDK

uv add litellm
from litellm import completion

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

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

</details> <details> <summary><b>Agents</b> - Invoke A2A Agents (Python SDK + AI Gateway)</summary>

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

Step 2. Call Agent via A2A SDK

from a2a.client import A2ACardResolver, A2AClient
from a2a.types import MessageSendParams, SendMessageRequest
from uuid import uuid4

base_url = "http://localhost:4000/a2a/my-agent"  # LiteLLM proxy + agent name
headers = {"Authorization": "Bearer sk-1234"}    # LiteLLM Virtual Key

async with httpx.AsyncClient(headers=headers) as httpx_client:
    resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url)
    agent_card = await resolver.get_agent_card()
    client = A2AClient(httpx_client=httpx_client, agent_card=agent_card)

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

Docs: A2A Agent Gateway

</details> <details> <summary><b>MCP Tools</b> - Connect MCP servers to any LLM (Python SDK + AI Gateway)</summary>

Python SDK - MCP Bridge

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

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
        )

AI Gateway - MCP Gateway

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 sk-1234' \\
  -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"
    }]
  }'

Use with Cursor IDE

{
  "mcpServers": {
    "LiteLLM": {
      "url": "http://localhost:4000/mcp/",
      "headers": {
        "x-litellm-api-key": "Bearer sk-1234"
      }
    }
  }
}

Docs: MCP Gateway

</details>

Supported Providers (Website Supported Models | Docs)

Provider/chat/completions/messages/responses/embeddings/image/generations/audio/transcriptions/audio/speech/moderations/batches/rerank
Abliteration (abliteration)
AI/ML API (aiml)
AI21 (ai21)
AI21 Chat (ai21_chat)
Aleph Alpha
Amazon Nova
Anthropic (anthropic)
Anthropic Text (anthropic_text)
Anyscale
AssemblyAI (assemblyai)
Auto Router (auto_router)
AWS - Bedrock (bedrock)
AWS - Sagemaker (sagemaker)
Azure (azure)
Azure AI (azure_ai)
Azure Text (azure_text)
Baseten (baseten)
Bytez (bytez)
Cerebras (cerebras)
Clarifai (clarifai)
Cloudflare AI Workers (cloudflare)
Codestral (codestral)
Cohere (cohere)
Cohere Chat (cohere_chat)
CometAPI (cometapi)
CompactifAI (compactifai)
Custom (custom)
Custom OpenAI (custom_openai)
Dashscope (dashscope)
Databricks (databricks)
DataRobot (datarobot)
Deepgram (deepgram)
DeepInfra (deepinfra)
Deepseek (deepseek)
ElevenLabs (elevenlabs)
Empower (empower)
Fal AI (fal_ai)
Featherless AI (featherless_ai)
Fireworks AI (fireworks_ai)
FriendliAI (friendliai)
Galadriel (galadriel)
GitHub Copilot (github_copilot)