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< h1 align = "center" >
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🚅 LiteLLM
< / h1 >
< p align = "center" >
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< p align = "center" >
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< a href = "https://render.com/deploy?repo=https://github.com/BerriAI/litellm" target = "_blank" rel = "nofollow" > < img src = "https://render.com/images/deploy-to-render-button.svg" alt = "Deploy to Render" > < / a >
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< a href = "https://railway.app/template/HLP0Ub?referralCode=jch2ME" >
< img src = "https://railway.app/button.svg" alt = "Deploy on Railway" >
< / a >
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< / p >
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< p align = "center" > Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
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< br >
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< / p >
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< h4 align = "center" > < a href = "https://docs.litellm.ai/docs/simple_proxy" target = "_blank" > LiteLLM Proxy Server (LLM Gateway)< / a > | < a href = "https://docs.litellm.ai/docs/hosted" target = "_blank" > Hosted Proxy (Preview)< / a > | < a href = "https://docs.litellm.ai/docs/enterprise" target = "_blank" > Enterprise Tier< / a > < / h4 >
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< h4 align = "center" >
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< a href = "https://pypi.org/project/litellm/" target = "_blank" >
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< img src = "https://img.shields.io/pypi/v/litellm.svg" alt = "PyPI Version" >
< / a >
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< a href = "https://www.ycombinator.com/companies/berriai" >
< img src = "https://img.shields.io/badge/Y%20Combinator-W23-orange?style=flat-square" alt = "Y Combinator W23" >
< / a >
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< a href = "https://wa.link/huol9n" >
< img src = "https://img.shields.io/static/v1?label=Chat%20on&message=WhatsApp&color=success&logo=WhatsApp&style=flat-square" alt = "Whatsapp" >
< / a >
< a href = "https://discord.gg/wuPM9dRgDw" >
< img src = "https://img.shields.io/static/v1?label=Chat%20on&message=Discord&color=blue&logo=Discord&style=flat-square" alt = "Discord" >
< / a >
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< a href = "https://www.litellm.ai/support" >
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< img src = "https://img.shields.io/static/v1?label=Chat%20on&message=Slack&color=black&logo=Slack&style=flat-square" alt = "Slack" >
< / a >
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< / h4 >
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LiteLLM manages:
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- Translate inputs to provider's `completion` , `embedding` , and `image_generation` endpoints
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- [Consistent output ](https://docs.litellm.ai/docs/completion/output ), text responses will always be available at `['choices'][0]['message']['content']`
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- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router ](https://docs.litellm.ai/docs/routing )
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- Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway) ](https://docs.litellm.ai/docs/simple_proxy )
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[**Jump to LiteLLM Proxy (LLM Gateway) Docs** ](https://github.com/BerriAI/litellm?tab=readme-ov-file#litellm-proxy-server-llm-gateway---docs ) < br >
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[**Jump to Supported LLM Providers** ](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs )
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🚨 **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 ](https://docs.litellm.ai/docs/proxy/release_cycle )
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Support for more providers. Missing a provider or LLM Platform, raise a [feature request ](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+ ).
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# Usage ([**Docs**](https://docs.litellm.ai/docs/))
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> [!IMPORTANT]
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> LiteLLM v1.0.0 now requires `openai>=1.0.0`. Migration guide [here](https://docs.litellm.ai/docs/migration)
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> LiteLLM v1.40.14+ now requires `pydantic>=2.0.0`. No changes required.
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< a target = "_blank" href = "https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb" >
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< img src = "https://colab.research.google.com/assets/colab-badge.svg" alt = "Open In Colab" / >
< / a >
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```shell
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pip install litellm
```
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```python
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from litellm import completion
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import os
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## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"
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os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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# openai call
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response = completion(model="openai/gpt-4o", messages=messages)
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# anthropic call
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response = completion(model="anthropic/claude-sonnet-4-20250514", messages=messages)
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print(response)
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```
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### Response (OpenAI Format)
```json
{
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"id": "chatcmpl-1214900a-6cdd-4148-b663-b5e2f642b4de",
"created": 1751494488,
"model": "claude-sonnet-4-20250514",
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"object": "chat.completion",
"system_fingerprint": null,
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
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"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?",
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"role": "assistant",
"tool_calls": null,
"function_call": null
}
}
],
"usage": {
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"completion_tokens": 39,
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"prompt_tokens": 13,
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"total_tokens": 52,
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"completion_tokens_details": null,
"prompt_tokens_details": {
"audio_tokens": null,
"cached_tokens": 0
},
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0
}
}
```
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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 ](https://docs.litellm.ai/docs/providers )
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## Async ([Docs](https://docs.litellm.ai/docs/completion/stream#async-completion))
```python
from litellm import acompletion
import asyncio
async def test_get_response():
user_message = "Hello, how are you?"
messages = [{"content": user_message, "role": "user"}]
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response = await acompletion(model="openai/gpt-4o", messages=messages)
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return response
response = asyncio.run(test_get_response())
print(response)
```
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## Streaming ([Docs](https://docs.litellm.ai/docs/completion/stream))
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liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response.
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Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.)
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```python
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from litellm import completion
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response = completion(model="openai/gpt-4o", messages=messages, stream=True)
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for part in response:
print(part.choices[0].delta.content or "")
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# claude sonnet 4
response = completion('anthropic/claude-sonnet-4-20250514', messages, stream=True)
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for part in response:
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print(part)
```
### Response chunk (OpenAI Format)
```json
{
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"id": "chatcmpl-fe575c37-5004-4926-ae5e-bfbc31f356ca",
"created": 1751494808,
"model": "claude-sonnet-4-20250514",
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"object": "chat.completion.chunk",
"system_fingerprint": null,
"choices": [
{
"finish_reason": null,
"index": 0,
"delta": {
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"provider_specific_fields": null,
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"content": "Hello",
"role": "assistant",
"function_call": null,
"tool_calls": null,
"audio": null
},
"logprobs": null
}
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],
"provider_specific_fields": null,
"stream_options": null,
"citations": null
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}
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```
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## Logging Observability ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
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LiteLLM exposes pre defined callbacks to send data to Lunary, MLflow, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack
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```python
from litellm import completion
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## set env variables for logging tools (when using MLflow, no API key set up is required)
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os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
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os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key"
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os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
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os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
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os.environ["OPENAI_API_KEY"] = "your-openai-key"
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# set callbacks
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litellm.success_callback = ["lunary", "mlflow", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc
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#openai call
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response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
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```
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# LiteLLM Proxy Server (LLM Gateway) - ([Docs](https://docs.litellm.ai/docs/simple_proxy))
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Track spend + Load Balance across multiple projects
[Hosted Proxy (Preview) ](https://docs.litellm.ai/docs/hosted )
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The proxy provides:
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1. [Hooks for auth ](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth )
2. [Hooks for logging ](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class )
3. [Cost tracking ](https://docs.litellm.ai/docs/proxy/virtual_keys#tracking-spend )
4. [Rate Limiting ](https://docs.litellm.ai/docs/proxy/users#set-rate-limits )
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## 📖 Proxy Endpoints - [Swagger Docs](https://litellm-api.up.railway.app/)
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## Quick Start Proxy - CLI
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```shell
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pip install 'litellm[proxy]'
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```
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### Step 1: Start litellm proxy
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```shell
$ litellm --model huggingface/bigcode/starcoder
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#INFO: Proxy running on http://0.0.0.0:4000
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```
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### Step 2: Make ChatCompletions Request to Proxy
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> [!IMPORTANT]
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> 💡 [Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl](https://docs.litellm.ai/docs/proxy/user_keys)
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```python
import openai # openai v1.0.0+
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client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
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# 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)
```
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## Proxy Key Management ([Docs](https://docs.litellm.ai/docs/proxy/virtual_keys))
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Connect the proxy with a Postgres DB to create proxy keys
```bash
# Get the code
git clone https://github.com/BerriAI/litellm
# Go to folder
cd litellm
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# Add the master key - you can change this after setup
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echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
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# Add the litellm salt key - you cannot change this after adding a model
# It is used to encrypt / decrypt your LLM API Key credentials
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# We recommend - https://1password.com/password-generator/
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# password generator to get a random hash for litellm salt key
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echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
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source .env
# Start
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docker compose up
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```
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UI on `/ui` on your proxy server
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Set budgets and rate limits across multiple projects
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`POST /key/generate`
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### Request
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```shell
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curl 'http://0.0.0.0:4000/key/generate' \
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--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"}}'
```
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### Expected Response
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```shell
{
"key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
"expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}
```
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## Supported Providers ([Docs](https://docs.litellm.ai/docs/providers))
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| Provider | [Completion ](https://docs.litellm.ai/docs/#basic-usage ) | [Streaming ](https://docs.litellm.ai/docs/completion/stream#streaming-responses ) | [Async Completion ](https://docs.litellm.ai/docs/completion/stream#async-completion ) | [Async Streaming ](https://docs.litellm.ai/docs/completion/stream#async-streaming ) | [Async Embedding ](https://docs.litellm.ai/docs/embedding/supported_embedding ) | [Async Image Generation ](https://docs.litellm.ai/docs/image_generation ) |
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|-------------------------------------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------|
| [openai ](https://docs.litellm.ai/docs/providers/openai ) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| [Meta - Llama API ](https://docs.litellm.ai/docs/providers/meta_llama ) | ✅ | ✅ | ✅ | ✅ | | |
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| [azure ](https://docs.litellm.ai/docs/providers/azure ) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| [AI/ML API ](https://docs.litellm.ai/docs/providers/aiml ) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| [aws - sagemaker ](https://docs.litellm.ai/docs/providers/aws_sagemaker ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [aws - bedrock ](https://docs.litellm.ai/docs/providers/bedrock ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [google - vertex_ai ](https://docs.litellm.ai/docs/providers/vertex ) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| [google - palm ](https://docs.litellm.ai/docs/providers/palm ) | ✅ | ✅ | ✅ | ✅ | | |
| [google AI Studio - gemini ](https://docs.litellm.ai/docs/providers/gemini ) | ✅ | ✅ | ✅ | ✅ | | |
| [mistral ai api ](https://docs.litellm.ai/docs/providers/mistral ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [cloudflare AI Workers ](https://docs.litellm.ai/docs/providers/cloudflare_workers ) | ✅ | ✅ | ✅ | ✅ | | |
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| [CompactifAI ](https://docs.litellm.ai/docs/providers/compactifai ) | ✅ | ✅ | ✅ | ✅ | | |
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| [cohere ](https://docs.litellm.ai/docs/providers/cohere ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [anthropic ](https://docs.litellm.ai/docs/providers/anthropic ) | ✅ | ✅ | ✅ | ✅ | | |
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| [empower ](https://docs.litellm.ai/docs/providers/empower ) | ✅ | ✅ | ✅ | ✅ |
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| [huggingface ](https://docs.litellm.ai/docs/providers/huggingface ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [replicate ](https://docs.litellm.ai/docs/providers/replicate ) | ✅ | ✅ | ✅ | ✅ | | |
| [together_ai ](https://docs.litellm.ai/docs/providers/togetherai ) | ✅ | ✅ | ✅ | ✅ | | |
| [openrouter ](https://docs.litellm.ai/docs/providers/openrouter ) | ✅ | ✅ | ✅ | ✅ | | |
| [ai21 ](https://docs.litellm.ai/docs/providers/ai21 ) | ✅ | ✅ | ✅ | ✅ | | |
| [baseten ](https://docs.litellm.ai/docs/providers/baseten ) | ✅ | ✅ | ✅ | ✅ | | |
| [vllm ](https://docs.litellm.ai/docs/providers/vllm ) | ✅ | ✅ | ✅ | ✅ | | |
| [nlp_cloud ](https://docs.litellm.ai/docs/providers/nlp_cloud ) | ✅ | ✅ | ✅ | ✅ | | |
| [aleph alpha ](https://docs.litellm.ai/docs/providers/aleph_alpha ) | ✅ | ✅ | ✅ | ✅ | | |
| [petals ](https://docs.litellm.ai/docs/providers/petals ) | ✅ | ✅ | ✅ | ✅ | | |
| [ollama ](https://docs.litellm.ai/docs/providers/ollama ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [deepinfra ](https://docs.litellm.ai/docs/providers/deepinfra ) | ✅ | ✅ | ✅ | ✅ | | |
| [perplexity-ai ](https://docs.litellm.ai/docs/providers/perplexity ) | ✅ | ✅ | ✅ | ✅ | | |
| [Groq AI ](https://docs.litellm.ai/docs/providers/groq ) | ✅ | ✅ | ✅ | ✅ | | |
| [Deepseek ](https://docs.litellm.ai/docs/providers/deepseek ) | ✅ | ✅ | ✅ | ✅ | | |
| [anyscale ](https://docs.litellm.ai/docs/providers/anyscale ) | ✅ | ✅ | ✅ | ✅ | | |
| [IBM - watsonx.ai ](https://docs.litellm.ai/docs/providers/watsonx ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [voyage ai ](https://docs.litellm.ai/docs/providers/voyage ) | | | | | ✅ | |
| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ | |
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| [FriendliAI ](https://docs.litellm.ai/docs/providers/friendliai ) | ✅ | ✅ | ✅ | ✅ | | |
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| [Galadriel ](https://docs.litellm.ai/docs/providers/galadriel ) | ✅ | ✅ | ✅ | ✅ | | |
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| [GradientAI ](https://docs.litellm.ai/docs/providers/gradient_ai ) | ✅ | ✅ | | | | |
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| [Novita AI ](https://novita.ai/models/llm?utm_source=github_litellm&utm_medium=github_readme&utm_campaign=github_link ) | ✅ | ✅ | ✅ | ✅ | | |
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| [Featherless AI ](https://docs.litellm.ai/docs/providers/featherless_ai ) | ✅ | ✅ | ✅ | ✅ | | |
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| [Nebius AI Studio ](https://docs.litellm.ai/docs/providers/nebius ) | ✅ | ✅ | ✅ | ✅ | ✅ | |
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| [Heroku ](https://docs.litellm.ai/docs/providers/heroku ) | ✅ | ✅ | | | | |
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| [OVHCloud AI Endpoints ](https://docs.litellm.ai/docs/providers/ovhcloud ) | ✅ | ✅ | | | | |
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[**Read the Docs** ](https://docs.litellm.ai/docs/ )
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## Run in Developer mode
### Services
1. Setup .env file in root
2. Run dependant services `docker-compose up db prometheus`
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### 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. Start proxy backend `python litellm/proxy_cli.py`
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### Frontend
1. Navigate to `ui/litellm-dashboard`
2. Install dependencies `npm install`
3. Run `npm run dev` to start the dashboard
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# Enterprise
For companies that need better security, user management and professional support
[Talk to founders ](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat )
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This covers:
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- ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/proxy/enterprise):**
- ✅ **Feature Prioritization**
- ✅ **Custom Integrations**
- ✅ **Professional Support - Dedicated discord + slack**
- ✅ **Custom SLAs**
- ✅ **Secure access with Single Sign-On**
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# Contributing
We welcome contributions to LiteLLM! Whether you're fixing bugs, adding features, or improving documentation, we appreciate your help.
## Quick Start for Contributors
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This requires poetry to be installed.
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```bash
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
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make format-check # Check formatting only
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```
For detailed contributing guidelines, see [CONTRIBUTING.md ](CONTRIBUTING.md ).
## Code Quality / Linting
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LiteLLM follows the [Google Python Style Guide ](https://google.github.io/styleguide/pyguide.html ).
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Our automated checks include:
- **Black** for code formatting
- **Ruff** for linting and code quality
- **MyPy** for type checking
- **Circular import detection**
- **Import safety checks**
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All these checks must pass before your PR can be merged.
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# Support / talk with founders
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- [Schedule Demo 👋 ](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version )
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- [Community Discord 💭 ](https://discord.gg/wuPM9dRgDw )
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- [Community Slack 💭 ](https://www.litellm.ai/support )
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- Our numbers 📞 +1 (770) 8783-106 /
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
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# Why did we build this
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- **Need for simplicity**: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.
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# Contributors
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