Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]
Docs | 100+ Supported Models |
LiteLLM manages
- Translating inputs to the provider's completion and embedding endpoints
- Guarantees consistent output, text responses will always be available at
['choices'][0]['message']['content']
- Exception mapping - common exceptions across providers are mapped to the OpenAI exception types
pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["COHERE_API_KEY"] = "your-cohere-key"
messages = [{ "content": "Hello, how are you?","role": "user"}]
# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)
# cohere call
response = completion(model="command-nightly", messages=messages)
print(response)
Streaming (Docs)
liteLLM supports streaming the model response back, pass stream=True
to get a streaming iterator in response.
Streaming is supported for OpenAI, Azure, Anthropic, Huggingface models
response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
for chunk in response:
print(chunk['choices'][0]['delta'])
# claude 2
result = completion('claude-2', messages, stream=True)
for chunk in result:
print(chunk['choices'][0]['delta'])
Caching (Docs)
LiteLLM supports caching completion()
and embedding()
calls for all LLMs. Hosted Cache LiteLLM API
import litellm
from litellm.caching import Cache
import os
litellm.cache = Cache()
os.environ['OPENAI_API_KEY'] = ""
# add to cache
response1 = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "why is LiteLLM amazing?"}],
caching=True
)
# returns cached response
response2 = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "why is LiteLLM amazing?"}],
caching=True
)
print(f"response1: {response1}")
print(f"response2: {response2}")
OpenAI Proxy Server (Docs)
Spin up a local server to translate openai api calls to any non-openai model (e.g. Huggingface, TogetherAI, Ollama, etc.)
This works for async + streaming as well.
litellm --model <model_name>
Running your model locally or on a custom endpoint ? Set the --api-base
parameter see how
Supported Provider (Docs)
Provider | Completion | Streaming | Async Completion | Async Streaming |
---|---|---|---|---|
openai | ✅ | ✅ | ✅ | ✅ |
cohere | ✅ | ✅ | ✅ | ✅ |
anthropic | ✅ | ✅ | ✅ | ✅ |
replicate | ✅ | ✅ | ✅ | ✅ |
huggingface | ✅ | ✅ | ✅ | ✅ |
together_ai | ✅ | ✅ | ✅ | ✅ |
openrouter | ✅ | ✅ | ✅ | ✅ |
vertex_ai | ✅ | ✅ | ✅ | ✅ |
palm | ✅ | ✅ | ✅ | ✅ |
ai21 | ✅ | ✅ | ✅ | ✅ |
baseten | ✅ | ✅ | ✅ | ✅ |
azure | ✅ | ✅ | ✅ | ✅ |
sagemaker | ✅ | ✅ | ✅ | ✅ |
bedrock | ✅ | ✅ | ✅ | ✅ |
vllm | ✅ | ✅ | ✅ | ✅ |
nlp_cloud | ✅ | ✅ | ✅ | ✅ |
aleph alpha | ✅ | ✅ | ✅ | ✅ |
petals | ✅ | ✅ | ✅ | ✅ |
ollama | ✅ | ✅ | ✅ | ✅ |
deepinfra | ✅ | ✅ | ✅ | ✅ |
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: Navigate into the project, and install dependencies:
cd litellm
poetry install
Step 3: Test your change:
cd litellm/tests # pwd: Documents/litellm/litellm/tests
pytest .
Step 4: Submit a PR with your changes! 🚀
- push your fork to your github repo
- submit a PR from there
Learn more on how to make a PR
- Schedule Demo 👋
- Community Discord 💭
- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
- Our emails ✉️ [email protected] / [email protected]
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere