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.

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-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)

{ "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 }

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

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 python litellm/proxy_cli.py

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

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.

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.

• 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.