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.

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

pip install litellm

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-3-sonnet-20240229", messages=messages) print(response)

{ "id": "chatcmpl-565d891b-a42e-4c39-8d14-82a1f5208885", "created": 1734366691, "model": "claude-3-sonnet-20240229", "object": "chat.completion", "system_fingerprint": null, "choices": [ { "finish_reason": "stop", "index": 0, "message": { "content": "Hello! As an AI language model, I don't have feelings, but I'm operating properly and ready to assist you with any questions or tasks you may have. How can I help you today?", "role": "assistant", "tool_calls": null, "function_call": null } } ], "usage": { "completion_tokens": 43, "prompt_tokens": 13, "total_tokens": 56, "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 2 response = completion('anthropic/claude-3-sonnet-20240229', messages, stream=True) for part in response: print(part)

{ "id": "chatcmpl-2be06597-eb60-4c70-9ec5-8cd2ab1b4697", "created": 1734366925, "model": "claude-3-sonnet-20240229", "object": "chat.completion.chunk", "system_fingerprint": null, "choices": [ { "finish_reason": null, "index": 0, "delta": { "content": "Hello", "role": "assistant", "function_call": null, "tool_calls": null, "audio": null }, "logprobs": null } ] }

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"}])

Track spend + Load Balance across multiple projects

Hosted Proxy (Preview)

The proxy provides:

1. Hooks for auth
2. Hooks for logging
3. Cost tracking
4. Rate Limiting

pip install 'litellm[proxy]'

$ litellm --model huggingface/bigcode/starcoder #INFO: Proxy running on http://0.0.0.0:4000

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)

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 }

Read the Docs

To contribute: Clone the repo locally -> Make a change -> Submit a PR with the change.

Here's how to modify the repo locally:

Step 1: Clone the repo

git clone https://github.com/BerriAI/litellm.git 

Step 2: Install dependencies:

pip install -r requirements.txt 

Step 3: Test your change:

a. Add a pytest test within tests/litellm/

This folder follows the same directory structure as litellm/.

If a corresponding test file does not exist, create one.

b. Run the test

cd tests/litellm # pwd: Documents/litellm/litellm/tests/litellm pytest /path/to/test_file.py 

Step 4: Submit a PR with your changes! 🚀

• push your fork to your GitHub repo
• submit a PR from there

Follow these instructions if you want to build / run the LiteLLM Docker Image yourself.

Step 1: Clone the repo

git clone https://github.com/BerriAI/litellm.git 

Step 2: Build the Docker Image

Build using Dockerfile.non_root

docker build -f docker/Dockerfile.non_root -t litellm_test_image . 

Step 3: Run the Docker Image

Make sure config.yaml is present in the root directory. This is your litellm proxy config file.

docker run \ -v $(pwd)/proxy_config.yaml:/app/config.yaml \ -e DATABASE_URL="postgresql://xxxxxxxx" \ -e LITELLM_MASTER_KEY="sk-1234" \ -p 4000:4000 \ litellm_test_image \ --config /app/config.yaml --detailed_debug 

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

LiteLLM follows the Google Python Style Guide.

We run:

• Ruff for formatting and linting checks
• Mypy + Pyright for typing 1, 2
• Black for formatting
• isort for import sorting

If you have suggestions on how to improve the code quality feel free to open an issue or a PR.

• Schedule Demo 👋
• Community Discord 💭
• 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.

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

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. Start proxy backend uvicorn litellm.proxy.proxy_server:app --host localhost --port 4000 --reload

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