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Showing content from https://github.com/BerriAI/litellm below:

GitHub - BerriAI/litellm: Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format

Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]

LiteLLM manages:

Translate inputs to provider's completion, embedding, and image_generation endpoints
Consistent output, text responses will always be available at ['choices'][0]['message']['content']
Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router
Set Budgets & Rate limits per project, api key, model LiteLLM Proxy Server (LLM Gateway)

Jump to LiteLLM 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.

Important

LiteLLM v1.0.0 now requires openai>=1.0.0. Migration guide here LiteLLM v1.40.14+ now requires pydantic>=2.0.0. No changes required.

from litellm import completion
import os

## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(model="openai/gpt-4o", messages=messages)

# anthropic call
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=messages)
print(response)

{
    "id": "chatcmpl-1214900a-6cdd-4148-b663-b5e2f642b4de",
    "created": 1751494488,
    "model": "claude-sonnet-4-20250514",
    "object": "chat.completion",
    "system_fingerprint": null,
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "Hello! I'm doing well, thank you for asking. I'm here and ready to help with whatever you'd like to discuss or work on. How are you doing today?",
                "role": "assistant",
                "tool_calls": null,
                "function_call": null
            }
        }
    ],
    "usage": {
        "completion_tokens": 39,
        "prompt_tokens": 13,
        "total_tokens": 52,
        "completion_tokens_details": null,
        "prompt_tokens_details": {
            "audio_tokens": null,
            "cached_tokens": 0
        },
        "cache_creation_input_tokens": 0,
        "cache_read_input_tokens": 0
    }
}

Call any model supported by a provider, with model=<provider_name>/<model_name>. There might be provider-specific details here, so refer to provider docs for more information

from litellm import acompletion
import asyncio

async def test_get_response():
    user_message = "Hello, how are you?"
    messages = [{"content": user_message, "role": "user"}]
    response = await acompletion(model="openai/gpt-4o", messages=messages)
    return response

response = asyncio.run(test_get_response())
print(response)

liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response. Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.)

from litellm import completion
response = completion(model="openai/gpt-4o", messages=messages, stream=True)
for part in response:
    print(part.choices[0].delta.content or "")

# claude sonnet 4
response = completion('anthropic/claude-sonnet-4-20250514', messages, stream=True)
for part in response:
    print(part)

Response chunk (OpenAI Format)

{
    "id": "chatcmpl-fe575c37-5004-4926-ae5e-bfbc31f356ca",
    "created": 1751494808,
    "model": "claude-sonnet-4-20250514",
    "object": "chat.completion.chunk",
    "system_fingerprint": null,
    "choices": [
        {
            "finish_reason": null,
            "index": 0,
            "delta": {
                "provider_specific_fields": null,
                "content": "Hello",
                "role": "assistant",
                "function_call": null,
                "tool_calls": null,
                "audio": null
            },
            "logprobs": null
        }
    ],
    "provider_specific_fields": null,
    "stream_options": null,
    "citations": null
}

Logging Observability (Docs)

LiteLLM exposes pre defined callbacks to send data to Lunary, MLflow, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack

from litellm import completion

## set env variables for logging tools (when using MLflow, no API key set up is required)
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"

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

# set callbacks
litellm.success_callback = ["lunary", "mlflow", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc

#openai call
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

LiteLLM Proxy Server (LLM Gateway) - (Docs)

Track spend + Load Balance across multiple projects

Hosted Proxy (Preview)

The proxy provides:

pip install 'litellm[proxy]'

Step 1: Start litellm proxy

$ litellm --model huggingface/bigcode/starcoder

#INFO: Proxy running on http://0.0.0.0:4000

Step 2: Make ChatCompletions Request to Proxy

import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

Proxy Key Management (Docs)

Connect the proxy with a Postgres DB to create proxy keys

# Get the code
git clone https://github.com/BerriAI/litellm

# Go to folder
cd litellm

# Add the master key - you can change this after setup
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env

# Add the litellm salt key - you cannot change this after adding a model
# It is used to encrypt / decrypt your LLM API Key credentials
# We recommend - https://1password.com/password-generator/
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' >> .env

source .env

# Start
docker-compose up

UI on /ui on your proxy server

Set budgets and rate limits across multiple projects POST /key/generate

curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data-raw '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m","metadata": {"user": "ishaan@berri.ai", "team": "core-infra"}}'

{
    "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
    "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}

Supported Providers (Docs)

Read the Docs

Interested in contributing? Contributions to LiteLLM Python SDK, Proxy Server, and LLM integrations are both accepted and highly encouraged!

Quick start: git clone → make install-dev → make format → make lint → make test-unit

See our comprehensive Contributing Guide (CONTRIBUTING.md) for detailed instructions.

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

Talk to founders

This covers:

✅ Features under the LiteLLM 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 poetry 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.

LiteLLM 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 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

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

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

(In root) create virtual environment python -m venv .venv
Activate virtual environment source .venv/bin/activate
Install dependencies pip install -e ".[all]"
Start proxy backend python3 /path/to/litellm/proxy_cli.py

Navigate to ui/litellm-dashboard
Install dependencies npm install
Run npm run dev to start the dashboard

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