hanzo-llm 1.81.15

Library to easily interface with LLM API providers

🚅 LLM

Call 100+ LLMs in OpenAI format. [Bedrock, Azure, OpenAI, VertexAI, Anthropic, Groq, etc.]

LLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier

Use LLM for

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

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

Python SDK

pip install llm

from llm 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

pip install 'llm[proxy]'
llm --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 llm.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
import httpx

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

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

Python SDK - MCP Bridge

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

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 LLM model
        response = await llm.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": "llm_proxy/mcp/github",
      "server_label": "github_mcp",
      "require_approval": "never"
    }]
  }'

Use with Cursor IDE

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

Docs: MCP Gateway

---

How to use LLM

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

	LLM AI Gateway	LLM Python SDK
Use Case	Central service (LLM Gateway) to access multiple LLMs	Use LLM 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.)

LLM Performance: 8ms P95 latency at 1k RPS (See benchmarks here)

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

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.

OSS Adopters

Netflix

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)	✅	✅	✅	✅
GitHub Models (github)	✅	✅	✅
Google - PaLM	✅	✅	✅
Google - Vertex AI (vertex_ai)	✅	✅	✅	✅	✅
Google AI Studio - Gemini (gemini)	✅	✅	✅
GradientAI (gradient_ai)	✅	✅	✅
Groq AI (groq)	✅	✅	✅
Heroku (heroku)	✅	✅	✅
Hosted VLLM (hosted_vllm)	✅	✅	✅
Huggingface (huggingface)	✅	✅	✅	✅						✅
Hyperbolic (hyperbolic)	✅	✅	✅
IBM - Watsonx.ai (watsonx)	✅	✅	✅	✅
Infinity (infinity)				✅
Jina AI (jina_ai)				✅
Lambda AI (lambda_ai)	✅	✅	✅
Lemonade (lemonade)	✅	✅	✅
LLM Proxy (llm_proxy)	✅	✅	✅	✅	✅
Llamafile (llamafile)	✅	✅	✅
LM Studio (lm_studio)	✅	✅	✅
Maritalk (maritalk)	✅	✅	✅
Meta - Llama API (meta_llama)	✅	✅	✅
Mistral AI API (mistral)	✅	✅	✅	✅
Moonshot (moonshot)	✅	✅	✅
Morph (morph)	✅	✅	✅
Nebius AI Studio (nebius)	✅	✅	✅	✅
NLP Cloud (nlp_cloud)	✅	✅	✅
Novita AI (novita)	✅	✅	✅
Nscale (nscale)	✅	✅	✅
Nvidia NIM (nvidia_nim)	✅	✅	✅
OCI (oci)	✅	✅	✅
Ollama (ollama)	✅	✅	✅	✅
Ollama Chat (ollama_chat)	✅	✅	✅
Oobabooga (oobabooga)	✅	✅	✅			✅	✅	✅	✅
OpenAI (openai)	✅	✅	✅	✅	✅	✅	✅	✅	✅
OpenAI-like (openai_like)				✅
OpenRouter (openrouter)	✅	✅	✅
OVHCloud AI Endpoints (ovhcloud)	✅	✅	✅
Perplexity AI (perplexity)	✅	✅	✅
Petals (petals)	✅	✅	✅
Predibase (predibase)	✅	✅	✅
Recraft (recraft)					✅
Replicate (replicate)	✅	✅	✅
Sagemaker Chat (sagemaker_chat)	✅	✅	✅
Sambanova (sambanova)	✅	✅	✅
Snowflake (snowflake)	✅	✅	✅
Text Completion Codestral (text-completion-codestral)	✅	✅	✅
Text Completion OpenAI (text-completion-openai)	✅	✅	✅			✅	✅	✅	✅
Together AI (together_ai)	✅	✅	✅
Topaz (topaz)	✅	✅	✅
Triton (triton)	✅	✅	✅
V0 (v0)	✅	✅	✅
Vercel AI Gateway (vercel_ai_gateway)	✅	✅	✅
VLLM (vllm)	✅	✅	✅
Volcengine (volcengine)	✅	✅	✅
Voyage AI (voyage)				✅
WandB Inference (wandb)	✅	✅	✅
Watsonx Text (watsonx_text)	✅	✅	✅
xAI (xai)	✅	✅	✅
Xinference (xinference)				✅

Read the Docs

Run in Developer mode

Services

1. Setup .env file in root
2. Run dependant services docker-compose up db prometheus

Backend

1. (In root) create virtual environment python -m venv .venv
2. Activate virtual environment source .venv/bin/activate
3. Install dependencies pip install -e ".[all]"
4. pip install prisma
5. prisma generate
6. Start proxy backend python llm/proxy/proxy_cli.py

Frontend

1. Navigate to ui/llm-dashboard
2. Install dependencies npm install
3. Run npm run dev to start the dashboard

Enterprise

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

Talk to founders

This covers:

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

Contributing

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

Quick Start for Contributors

This requires poetry to be installed.

git clone https://github.com/BerriAI/llm.git
cd llm
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.

Code Quality / Linting

LLM follows the Google Python Style Guide.

Our automated checks include:

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

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

Support / talk with founders

• Schedule Demo 👋
• Community Discord 💭
• Community Slack 💭
• Our numbers 📞 [phone] / ‭[phone]‬
• Our emails ✉️ [e-mail] / [e-mail]

Why did we build this

• Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.

Contributors