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Router - Load Balancing, Fallbacks

LiteLLM manages:

  • Load-balance across multiple deployments (e.g. Azure/OpenAI)
  • Prioritizing important requests to ensure they don't fail (i.e. Queueing)
  • Basic reliability logic - cooldowns, fallbacks, timeouts and retries (fixed + exponential backoff) across multiple deployments/providers.

In production, litellm supports using Redis as a way to track cooldown server and usage (managing tpm/rpm limits).

info

If you want a server to load balance across different LLM APIs, use our OpenAI Proxy Server

Load Balancing

(s/o @paulpierre and sweep proxy for their contributions to this implementation) See Code

Quick Start

from litellm import Router

model_list = [{ # list of model deployments
"model_name": "gpt-3.5-turbo", # model alias -> loadbalance between models with same `model_name`
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2", # actual model name
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
}
}, {
"model_name": "gpt-3.5-turbo",
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-functioncalling",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
}
}, {
"model_name": "gpt-3.5-turbo",
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
}
}, {
"model_name": "gpt-4",
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/gpt-4",
"api_key": os.getenv("AZURE_API_KEY"),
"api_base": os.getenv("AZURE_API_BASE"),
"api_version": os.getenv("AZURE_API_VERSION"),
}
}, {
"model_name": "gpt-4",
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-4",
"api_key": os.getenv("OPENAI_API_KEY"),
}
},

]

router = Router(model_list=model_list)

# openai.ChatCompletion.create replacement
# requests with model="gpt-3.5-turbo" will pick a deployment where model_name="gpt-3.5-turbo"
response = await router.acompletion(model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey, how's it going?"}])

print(response)

# openai.ChatCompletion.create replacement
# requests with model="gpt-4" will pick a deployment where model_name="gpt-4"
response = await router.acompletion(model="gpt-4",
messages=[{"role": "user", "content": "Hey, how's it going?"}])

print(response)

Available Endpoints

  • router.completion() - chat completions endpoint to call 100+ LLMs
  • router.acompletion() - async chat completion calls
  • router.embeddings() - embedding endpoint for Azure, OpenAI, Huggingface endpoints
  • router.aembeddings() - async embeddings calls
  • router.text_completion() - completion calls in the old OpenAI /v1/completions endpoint format
  • router.atext_completion() - async text completion calls
  • router.image_generation() - completion calls in OpenAI /v1/images/generations endpoint format
  • router.aimage_generation() - async image generation calls

Advanced - Routing Strategies

Routing Strategies - Weighted Pick, Rate Limit Aware, Least Busy, Latency Based, Cost Based

Router provides 4 strategies for routing your calls across multiple deployments:

🎉 NEW This is an async implementation of usage-based-routing.

Filters out deployment if tpm/rpm limit exceeded - If you pass in the deployment's tpm/rpm limits.

Routes to deployment with lowest TPM usage for that minute.

In production, we use Redis to track usage (TPM/RPM) across multiple deployments. This implementation uses async redis calls (redis.incr and redis.mget).

For Azure, your RPM = TPM/6.

from litellm import Router 


model_list = [{ # list of model deployments
"model_name": "gpt-3.5-turbo", # model alias
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2", # actual model name
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 100000,
"rpm": 10000,
}, {
"model_name": "gpt-3.5-turbo",
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-functioncalling",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 100000,
"rpm": 1000,
}, {
"model_name": "gpt-3.5-turbo",
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 100000,
"rpm": 1000,
}]
router = Router(model_list=model_list,
redis_host=os.environ["REDIS_HOST"],
redis_password=os.environ["REDIS_PASSWORD"],
redis_port=os.environ["REDIS_PORT"],
routing_strategy="usage-based-routing-v2" # 👈 KEY CHANGE
enable_pre_call_check=True, # enables router rate limits for concurrent calls
)

response = await router.acompletion(model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey, how's it going?"}]

print(response)

Basic Reliability

Max Parallel Requests (ASYNC)

Used in semaphore for async requests on router. Limit the max concurrent calls made to a deployment. Useful in high-traffic scenarios.

If tpm/rpm is set, and no max parallel request limit given, we use the RPM or calculated RPM (tpm/1000/6) as the max parallel request limit.

from litellm import Router 

model_list = [{
"model_name": "gpt-4",
"litellm_params": {
"model": "azure/gpt-4",
...
"max_parallel_requests": 10 # 👈 SET PER DEPLOYMENT
}
}]

### OR ###

router = Router(model_list=model_list, default_max_parallel_requests=20) # 👈 SET DEFAULT MAX PARALLEL REQUESTS


# deployment max parallel requests > default max parallel requests

See Code

Timeouts

The timeout set in router is for the entire length of the call, and is passed down to the completion() call level as well.

Global Timeouts

from litellm import Router 

model_list = [{...}]

router = Router(model_list=model_list,
timeout=30) # raise timeout error if call takes > 30s

print(response)

Timeouts per model

from litellm import Router 
import asyncio

model_list = [{
"model_name": "gpt-3.5-turbo",
"litellm_params": {
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
"timeout": 300 # sets a 5 minute timeout
"stream_timeout": 30 # sets a 30s timeout for streaming calls
}
}]

# init router
router = Router(model_list=model_list, routing_strategy="least-busy")
async def router_acompletion():
response = await router.acompletion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey, how's it going?"}]
)
print(response)
return response

asyncio.run(router_acompletion())

Cooldowns

Set the limit for how many calls a model is allowed to fail in a minute, before being cooled down for a minute.

from litellm import Router

model_list = [{...}]

router = Router(model_list=model_list,
allowed_fails=1, # cooldown model if it fails > 1 call in a minute.
cooldown_time=100 # cooldown the deployment for 100 seconds if it num_fails > allowed_fails
)

user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]

# normal call
response = router.completion(model="gpt-3.5-turbo", messages=messages)

print(f"response: {response}")

Retries

For both async + sync functions, we support retrying failed requests.

For RateLimitError we implement exponential backoffs

For generic errors, we retry immediately

Here's a quick look at how we can set num_retries = 3:

from litellm import Router

model_list = [{...}]

router = Router(model_list=model_list,
num_retries=3)

user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]

# normal call
response = router.completion(model="gpt-3.5-turbo", messages=messages)

print(f"response: {response}")

We also support setting minimum time to wait before retrying a failed request. This is via the retry_after param.

from litellm import Router

model_list = [{...}]

router = Router(model_list=model_list,
num_retries=3, retry_after=5) # waits min 5s before retrying request

user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]

# normal call
response = router.completion(model="gpt-3.5-turbo", messages=messages)

print(f"response: {response}")

Retries based on Error Type

Use RetryPolicy if you want to set a num_retries based on the Exception receieved

Example:

  • 4 retries for ContentPolicyViolationError
  • 0 retries for RateLimitErrors

Example Usage

from litellm.router import RetryPolicy
retry_policy = RetryPolicy(
ContentPolicyViolationErrorRetries=3, # run 3 retries for ContentPolicyViolationErrors
AuthenticationErrorRetries=0, # run 0 retries for AuthenticationErrorRetries
BadRequestErrorRetries=1,
TimeoutErrorRetries=2,
RateLimitErrorRetries=3,
)

router = litellm.Router(
model_list=[
{
"model_name": "gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
},
},
{
"model_name": "bad-model", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
},
},
],
retry_policy=retry_policy,
)

response = await router.acompletion(
model=model,
messages=messages,
)

Fallbacks

If a call fails after num_retries, fall back to another model group.

If the error is a context window exceeded error, fall back to a larger model group (if given).

Fallbacks are done in-order - ["gpt-3.5-turbo, "gpt-4", "gpt-4-32k"], will do 'gpt-3.5-turbo' first, then 'gpt-4', etc.

You can also set 'default_fallbacks', in case a specific model group is misconfigured / bad.

from litellm import Router

model_list = [
{ # list of model deployments
"model_name": "azure/gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 240000,
"rpm": 1800
},
{ # list of model deployments
"model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 240000,
"rpm": 1800
},
{
"model_name": "azure/gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-functioncalling",
"api_key": "bad-key",
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 240000,
"rpm": 1800
},
{
"model_name": "gpt-3.5-turbo", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 1000000,
"rpm": 9000
},
{
"model_name": "gpt-3.5-turbo-16k", # openai model name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo-16k",
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 1000000,
"rpm": 9000
}
]


router = Router(model_list=model_list,
fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}],
default_fallbacks=["gpt-3.5-turbo-16k"],
context_window_fallbacks=[{"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}],
set_verbose=True)


user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]

# normal fallback call
response = router.completion(model="azure/gpt-3.5-turbo", messages=messages)

# context window fallback call
response = router.completion(model="azure/gpt-3.5-turbo-context-fallback", messages=messages)

print(f"response: {response}")

Caching

In production, we recommend using a Redis cache. For quickly testing things locally, we also support simple in-memory caching.

In-memory Cache

router = Router(model_list=model_list, 
cache_responses=True)

print(response)

Redis Cache

router = Router(model_list=model_list, 
redis_host=os.getenv("REDIS_HOST"),
redis_password=os.getenv("REDIS_PASSWORD"),
redis_port=os.getenv("REDIS_PORT"),
cache_responses=True)

print(response)

Pass in Redis URL, additional kwargs

router = Router(model_list: Optional[list] = None,
## CACHING ##
redis_url=os.getenv("REDIS_URL")",
cache_kwargs= {}, # additional kwargs to pass to RedisCache (see caching.py)
cache_responses=True)

Pre-Call Checks (Context Window, EU-Regions)

Enable pre-call checks to filter out:

  1. deployments with context window limit < messages for a call.
  2. deployments outside of eu-region

1. Enable pre-call checks

from litellm import Router 
# ...
router = Router(model_list=model_list, enable_pre_call_checks=True) # 👈 Set to True

2. Set Model List

For context window checks on azure deployments, set the base model. Pick the base model from this list, all the azure models start with azure/.

For 'eu-region' filtering, Set 'region_name' of deployment.

Note: We automatically infer region_name for Vertex AI, Bedrock, and IBM WatsonxAI based on your litellm params. For Azure, set litellm.enable_preview = True.

See Code

model_list = [
{
"model_name": "gpt-3.5-turbo", # model group name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
"region_name": "eu" # 👈 SET 'EU' REGION NAME
"base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL
},
},
{
"model_name": "gpt-3.5-turbo", # model group name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo-1106",
"api_key": os.getenv("OPENAI_API_KEY"),
},
},
{
"model_name": "gemini-pro",
"litellm_params: {
"model": "vertex_ai/gemini-pro-1.5",
"vertex_project": "adroit-crow-1234",
"vertex_location": "us-east1" # 👈 AUTOMATICALLY INFERS 'region_name'
}
}
]

router = Router(model_list=model_list, enable_pre_call_checks=True)

3. Test it!

"""
- Give a gpt-3.5-turbo model group with different context windows (4k vs. 16k)
- Send a 5k prompt
- Assert it works
"""
from litellm import Router
import os

model_list = [
{
"model_name": "gpt-3.5-turbo", # model group name
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
"base_model": "azure/gpt-35-turbo",
},
"model_info": {
"base_model": "azure/gpt-35-turbo",
}
},
{
"model_name": "gpt-3.5-turbo", # model group name
"litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo-1106",
"api_key": os.getenv("OPENAI_API_KEY"),
},
},
]

router = Router(model_list=model_list, enable_pre_call_checks=True)

text = "What is the meaning of 42?" * 5000

response = router.completion(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": text},
{"role": "user", "content": "Who was Alexander?"},
],
)

print(f"response: {response}")

Caching across model groups

If you want to cache across 2 different model groups (e.g. azure deployments, and openai), use caching groups.

import litellm, asyncio, time
from litellm import Router

# set os env
os.environ["OPENAI_API_KEY"] = ""
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

async def test_acompletion_caching_on_router_caching_groups():
# tests acompletion + caching on router
try:
litellm.set_verbose = True
model_list = [
{
"model_name": "openai-gpt-3.5-turbo",
"litellm_params": {
"model": "gpt-3.5-turbo-0613",
"api_key": os.getenv("OPENAI_API_KEY"),
},
},
{
"model_name": "azure-gpt-3.5-turbo",
"litellm_params": {
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_base": os.getenv("AZURE_API_BASE"),
"api_version": os.getenv("AZURE_API_VERSION")
},
}
]

messages = [
{"role": "user", "content": f"write a one sentence poem {time.time()}?"}
]
start_time = time.time()
router = Router(model_list=model_list,
cache_responses=True,
caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")])
response1 = await router.acompletion(model="openai-gpt-3.5-turbo", messages=messages, temperature=1)
print(f"response1: {response1}")
await asyncio.sleep(1) # add cache is async, async sleep for cache to get set
response2 = await router.acompletion(model="azure-gpt-3.5-turbo", messages=messages, temperature=1)
assert response1.id == response2.id
assert len(response1.choices[0].message.content) > 0
assert response1.choices[0].message.content == response2.choices[0].message.content
except Exception as e:
traceback.print_exc()

asyncio.run(test_acompletion_caching_on_router_caching_groups())

Alerting 🚨

Send alerts to slack / your webhook url for the following events

  • LLM API Exceptions
  • Slow LLM Responses

Get a slack webhook url from https://api.slack.com/messaging/webhooks

Usage

Initialize an AlertingConfig and pass it to litellm.Router. The following code will trigger an alert because api_key=bad-key which is invalid

from litellm.router import AlertingConfig
import litellm
import os

router = litellm.Router(
model_list=[
{
"model_name": "gpt-3.5-turbo",
"litellm_params": {
"model": "gpt-3.5-turbo",
"api_key": "bad_key",
},
}
],
alerting_config= AlertingConfig(
alerting_threshold=10, # threshold for slow / hanging llm responses (in seconds). Defaults to 300 seconds
webhook_url= os.getenv("SLACK_WEBHOOK_URL") # webhook you want to send alerts to
),
)
try:
await router.acompletion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)
except:
pass

Track cost for Azure Deployments

Problem: Azure returns gpt-4 in the response when azure/gpt-4-1106-preview is used. This leads to inaccurate cost tracking

Solution ✅ : Set model_info["base_model"] on your router init so litellm uses the correct model for calculating azure cost

Step 1. Router Setup

from litellm import Router

model_list = [
{ # list of model deployments
"model_name": "gpt-4-preview", # model alias
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-v-2", # actual model name
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"model_info": {
"base_model": "azure/gpt-4-1106-preview" # azure/gpt-4-1106-preview will be used for cost tracking, ensure this exists in litellm model_prices_and_context_window.json
}
},
{
"model_name": "gpt-4-32k",
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/chatgpt-functioncalling",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"model_info": {
"base_model": "azure/gpt-4-32k" # azure/gpt-4-32k will be used for cost tracking, ensure this exists in litellm model_prices_and_context_window.json
}
}
]

router = Router(model_list=model_list)

Step 2. Access response_cost in the custom callback, litellm calculates the response cost for you

import litellm
from litellm.integrations.custom_logger import CustomLogger

class MyCustomHandler(CustomLogger):
def log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Success")
response_cost = kwargs.get("response_cost")
print("response_cost=", response_cost)

customHandler = MyCustomHandler()
litellm.callbacks = [customHandler]

# router completion call
response = router.completion(
model="gpt-4-32k",
messages=[{ "role": "user", "content": "Hi who are you"}]
)

Default litellm.completion/embedding params

You can also set default params for litellm completion/embedding calls. Here's how to do that:

from litellm import Router

fallback_dict = {"gpt-3.5-turbo": "gpt-3.5-turbo-16k"}

router = Router(model_list=model_list,
default_litellm_params={"context_window_fallback_dict": fallback_dict})

user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]

# normal call
response = router.completion(model="gpt-3.5-turbo", messages=messages)

print(f"response: {response}")

Custom Callbacks - Track API Key, API Endpoint, Model Used

If you need to track the api_key, api endpoint, model, custom_llm_provider used for each completion call, you can setup a custom callback

Usage

import litellm
from litellm.integrations.custom_logger import CustomLogger

class MyCustomHandler(CustomLogger):
def log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Success")
print("kwargs=", kwargs)
litellm_params= kwargs.get("litellm_params")
api_key = litellm_params.get("api_key")
api_base = litellm_params.get("api_base")
custom_llm_provider= litellm_params.get("custom_llm_provider")
response_cost = kwargs.get("response_cost")

# print the values
print("api_key=", api_key)
print("api_base=", api_base)
print("custom_llm_provider=", custom_llm_provider)
print("response_cost=", response_cost)

def log_failure_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Failure")
print("kwargs=")

customHandler = MyCustomHandler()

litellm.callbacks = [customHandler]

# Init Router
router = Router(model_list=model_list, routing_strategy="simple-shuffle")

# router completion call
response = router.completion(
model="gpt-3.5-turbo",
messages=[{ "role": "user", "content": "Hi who are you"}]
)

Deploy Router

If you want a server to load balance across different LLM APIs, use our OpenAI Proxy Server

Init Params for the litellm.Router

def __init__(
model_list: Optional[list] = None,

## CACHING ##
redis_url: Optional[str] = None,
redis_host: Optional[str] = None,
redis_port: Optional[int] = None,
redis_password: Optional[str] = None,
cache_responses: Optional[bool] = False,
cache_kwargs: dict = {}, # additional kwargs to pass to RedisCache (see caching.py)
caching_groups: Optional[
List[tuple]
] = None, # if you want to cache across model groups
client_ttl: int = 3600, # ttl for cached clients - will re-initialize after this time in seconds

## RELIABILITY ##
num_retries: int = 0,
timeout: Optional[float] = None,
default_litellm_params={}, # default params for Router.chat.completion.create
fallbacks: Optional[List] = None,
default_fallbacks: Optional[List] = None
allowed_fails: Optional[int] = None, # Number of times a deployment can failbefore being added to cooldown
cooldown_time: float = 1, # (seconds) time to cooldown a deployment after failure
context_window_fallbacks: Optional[List] = None,
model_group_alias: Optional[dict] = {},
retry_after: int = 0, # (min) time to wait before retrying a failed request
routing_strategy: Literal[
"simple-shuffle",
"least-busy",
"usage-based-routing",
"latency-based-routing",
"cost-based-routing",
] = "simple-shuffle",

## DEBUGGING ##
set_verbose: bool = False, # set this to True for seeing logs
debug_level: Literal["DEBUG", "INFO"] = "INFO", # set this to "DEBUG" for detailed debugging
):

Debugging Router

Basic Debugging

Set Router(set_verbose=True)

from litellm import Router

router = Router(
model_list=model_list,
set_verbose=True
)

Detailed Debugging

Set Router(set_verbose=True,debug_level="DEBUG")

from litellm import Router

router = Router(
model_list=model_list,
set_verbose=True,
debug_level="DEBUG" # defaults to INFO
)

Very Detailed Debugging

Set litellm.set_verbose=True and Router(set_verbose=True,debug_level="DEBUG")

from litellm import Router
import litellm

litellm.set_verbose = True

router = Router(
model_list=model_list,
set_verbose=True,
debug_level="DEBUG" # defaults to INFO
)