BerriAI/litellm

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

Python SDK

uv add litellm

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

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 "}    # 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

Python SDK - MCP Bridge

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
        )

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 ' \
  -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 "
      }
    }
  }
}

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

Python SDK - Agents

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