litellm/docs/my-website/docs/proxy/configs.md

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Proxy Config.yaml

Set model list, api_base, api_key, temperature & proxy server settings (master-key) on the config.yaml.

Param Name	Description
model_list	List of supported models on the server, with model-specific configs
router_settings	litellm Router settings, example routing_strategy="least-busy" see all
litellm_settings	litellm Module settings, example litellm.drop_params=True, litellm.set_verbose=True, litellm.api_base, litellm.cache see all
general_settings	Server settings, example setting master_key: sk-my_special_key
environment_variables	Environment Variables example, REDIS_HOST, REDIS_PORT

Complete List: Check the Swagger UI docs on <your-proxy-url>/#/config.yaml (e.g. http://0.0.0.0:4000/#/config.yaml), for everything you can pass in the config.yaml.

Quick Start

Set a model alias for your deployments.

In the config.yaml the model_name parameter is the user-facing name to use for your deployment.

In the config below:

• model_name: the name to pass TO litellm from the external client
• litellm_params.model: the model string passed to the litellm.completion() function

E.g.:

• model=vllm-models will route to openai/facebook/opt-125m.
• model=gpt-3.5-turbo will load balance between azure/gpt-turbo-small-eu and azure/gpt-turbo-small-ca

model_list:
  - model_name: gpt-3.5-turbo ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: azure/gpt-turbo-small-eu ### MODEL NAME sent to `litellm.completion()` ###
      api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
      api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
      rpm: 6      # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm)
  - model_name: bedrock-claude-v1 
    litellm_params:
      model: bedrock/anthropic.claude-instant-v1
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/gpt-turbo-small-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: "os.environ/AZURE_API_KEY_CA"
      rpm: 6
  - model_name: anthropic-claude
    litellm_params: 
      model: bedrock/anthropic.claude-instant-v1
      ### [OPTIONAL] SET AWS REGION ###
      aws_region_name: us-east-1
  - model_name: vllm-models
    litellm_params:
      model: openai/facebook/opt-125m # the `openai/` prefix tells litellm it's openai compatible
      api_base: http://0.0.0.0:4000/v1
      api_key: none
      rpm: 1440
    model_info: 
      version: 2
  
  # Use this if you want to make requests to `claude-3-haiku-20240307`,`claude-3-opus-20240229`,`claude-2.1` without defining them on the config.yaml
  # Default models
  # Works for ALL Providers and needs the default provider credentials in .env
  - model_name: "*" 
    litellm_params:
      model: "*"

litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py
  drop_params: True
  success_callback: ["langfuse"] # OPTIONAL - if you want to start sending LLM Logs to Langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your env

general_settings: 
  master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234)
  alerting: ["slack"] # [OPTIONAL] If you want Slack Alerts for Hanging LLM requests, Slow llm responses, Budget Alerts. Make sure to set `SLACK_WEBHOOK_URL` in your env

:::info

For more provider-specific info, go here

:::

Step 2: Start Proxy with config

$ litellm --config /path/to/config.yaml

:::tip

Run with --detailed_debug if you need detailed debug logs

$ litellm --config /path/to/config.yaml --detailed_debug

:::

Step 3: Test it

Sends request to model where model_name=gpt-3.5-turbo on config.yaml.

If multiple with model_name=gpt-3.5-turbo does Load Balancing

Langchain, OpenAI SDK Usage Examples

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "gpt-3.5-turbo",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

LLM configs model_list

Model-specific params (API Base, Keys, Temperature, Max Tokens, Organization, Headers etc.)

You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc.

All input params

Step 1: Create a config.yaml file

model_list:
  - model_name: gpt-4-team1
    litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body
      model: azure/chatgpt-v-2
      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
      api_version: "2023-05-15"
      azure_ad_token: eyJ0eXAiOiJ
      seed: 12
      max_tokens: 20
  - model_name: gpt-4-team2
    litellm_params:
      model: azure/gpt-4
      api_key: sk-123
      api_base: https://openai-gpt-4-test-v-2.openai.azure.com/
      temperature: 0.2
  - model_name: openai-gpt-3.5
    litellm_params:
      model: openai/gpt-3.5-turbo
      extra_headers: {"AI-Resource Group": "ishaan-resource"}
      api_key: sk-123
      organization: org-ikDc4ex8NB
      temperature: 0.2
  - model_name: mistral-7b
    litellm_params:
      model: ollama/mistral
      api_base: your_ollama_api_base

Step 2: Start server with config

$ litellm --config /path/to/config.yaml

Expected Logs:

Look for this line in your console logs to confirm the config.yaml was loaded in correctly.

LiteLLM: Proxy initialized with Config, Set models:

Embedding Models - Use Sagemaker, Bedrock, Azure, OpenAI, XInference

See supported Embedding Providers & Models here

model_list:
  - model_name: bedrock-cohere
    litellm_params:
      model: "bedrock/cohere.command-text-v14"
      aws_region_name: "us-west-2"
  - model_name: bedrock-cohere
    litellm_params:
      model: "bedrock/cohere.command-text-v14"
      aws_region_name: "us-east-2"
  - model_name: bedrock-cohere
    litellm_params:
      model: "bedrock/cohere.command-text-v14"
      aws_region_name: "us-east-1"

Here's how to route between GPT-J embedding (sagemaker endpoint), Amazon Titan embedding (Bedrock) and Azure OpenAI embedding on the proxy server:

model_list:
  - model_name: sagemaker-embeddings
    litellm_params: 
      model: "sagemaker/berri-benchmarking-gpt-j-6b-fp16"
  - model_name: amazon-embeddings
    litellm_params:
      model: "bedrock/amazon.titan-embed-text-v1"
  - model_name: azure-embeddings
    litellm_params: 
      model: "azure/azure-embedding-model"
      api_base: "os.environ/AZURE_API_BASE" # os.getenv("AZURE_API_BASE")
      api_key: "os.environ/AZURE_API_KEY" # os.getenv("AZURE_API_KEY")
      api_version: "2023-07-01-preview"

general_settings:
  master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token

LiteLLM Proxy supports all Feature-Extraction Embedding models.

model_list:
  - model_name: deployed-codebert-base
    litellm_params: 
      # send request to deployed hugging face inference endpoint
      model: huggingface/microsoft/codebert-base # add huggingface prefix so it routes to hugging face
      api_key: hf_LdS                            # api key for hugging face inference endpoint
      api_base: https://uysneno1wv2wd4lw.us-east-1.aws.endpoints.huggingface.cloud # your hf inference endpoint 
  - model_name: codebert-base
    litellm_params: 
      # no api_base set, sends request to hugging face free inference api https://api-inference.huggingface.co/models/
      model: huggingface/microsoft/codebert-base # add huggingface prefix so it routes to hugging face
      api_key: hf_LdS                            # api key for hugging face                     

model_list:
  - model_name: azure-embedding-model # model group
    litellm_params:
      model: azure/azure-embedding-model # model name for litellm.embedding(model=azure/azure-embedding-model) call
      api_base: your-azure-api-base
      api_key: your-api-key
      api_version: 2023-07-01-preview

model_list:
- model_name: text-embedding-ada-002 # model group
  litellm_params:
    model: text-embedding-ada-002 # model name for litellm.embedding(model=text-embedding-ada-002) 
    api_key: your-api-key-1
- model_name: text-embedding-ada-002 
  litellm_params:
    model: text-embedding-ada-002
    api_key: your-api-key-2

https://docs.litellm.ai/docs/providers/xinference

Note add xinference/ prefix to litellm_params: model so litellm knows to route to OpenAI

model_list:
- model_name: embedding-model  # model group
  litellm_params:
    model: xinference/bge-base-en   # model name for litellm.embedding(model=xinference/bge-base-en) 
    api_base: http://0.0.0.0:9997/v1

Use this for calling /embedding endpoints on OpenAI Compatible Servers.

Note add openai/ prefix to litellm_params: model so litellm knows to route to OpenAI

model_list:
- model_name: text-embedding-ada-002  # model group
  litellm_params:
    model: openai/<your-model-name>   # model name for litellm.embedding(model=text-embedding-ada-002) 
    api_base: <model-api-base>

Start Proxy

litellm --config config.yaml

Make Request

Sends Request to bedrock-cohere

curl --location 'http://0.0.0.0:4000/chat/completions' \
  --header 'Content-Type: application/json' \
  --data ' {
  "model": "bedrock-cohere",
  "messages": [
      {
      "role": "user",
      "content": "gm"
      }
  ]
}'

Multiple OpenAI Organizations

Add all openai models across all OpenAI organizations with just 1 model definition

  - model_name: *
    litellm_params:
      model: openai/*
      api_key: os.environ/OPENAI_API_KEY
      organization:
       - org-1 
       - org-2 
       - org-3

LiteLLM will automatically create separate deployments for each org.

Confirm this via

curl --location 'http://0.0.0.0:4000/v1/model/info' \
--header 'Authorization: Bearer ${LITELLM_KEY}' \
--data ''

Provider specific wildcard routing

Proxy all models from a provider

Use this if you want to proxy all models from a specific provider without defining them on the config.yaml

Step 1 - define provider specific routing on config.yaml

model_list:
  # provider specific wildcard routing
  - model_name: "anthropic/*"
    litellm_params:
      model: "anthropic/*"
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: "groq/*"
    litellm_params:
      model: "groq/*"
      api_key: os.environ/GROQ_API_KEY

Step 2 - Run litellm proxy

$ litellm --config /path/to/config.yaml

Step 3 Test it

Test with anthropic/ - all models with anthropic/ prefix will get routed to anthropic/*

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "anthropic/claude-3-sonnet-20240229",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

Test with groq/ - all models with groq/ prefix will get routed to groq/*

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "groq/llama3-8b-8192",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

Load Balancing

:::info For more on this, go to this page :::

Use this to call multiple instances of the same model and configure things like routing strategy.

For optimal performance:

• Set tpm/rpm per model deployment. Weighted picks are then based on the established tpm/rpm.
• Select your optimal routing strategy in router_settings:routing_strategy.

LiteLLM supports

["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"`

When tpm/rpm is set + routing_strategy==simple-shuffle litellm will use a weighted pick based on set tpm/rpm. In our load tests setting tpm/rpm for all deployments + routing_strategy==simple-shuffle maximized throughput

• When using multiple LiteLLM Servers / Kubernetes set redis settings router_settings:redis_host etc

model_list:
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8001
        rpm: 60      # Optional[int]: When rpm/tpm set - litellm uses weighted pick for load balancing. rpm = Rate limit for this deployment: in requests per minute (rpm).
        tpm: 1000   # Optional[int]: tpm = Tokens Per Minute 
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8002
        rpm: 600      
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8003
        rpm: 60000      
  - model_name: gpt-3.5-turbo
    litellm_params:
        model: gpt-3.5-turbo
        api_key: <my-openai-key>
        rpm: 200      
  - model_name: gpt-3.5-turbo-16k
    litellm_params:
        model: gpt-3.5-turbo-16k
        api_key: <my-openai-key>
        rpm: 100      

litellm_settings:
  num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta)
  request_timeout: 10 # raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout 
  fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries 
  context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error
  allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. 

router_settings: # router_settings are optional
  routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
  model_group_alias: {"gpt-4": "gpt-3.5-turbo"} # all requests with `gpt-4` will be routed to models with `gpt-3.5-turbo`
  num_retries: 2
  timeout: 30                                  # 30 seconds
  redis_host: <your redis host>                # set this when using multiple litellm proxy deployments, load balancing state stored in redis
  redis_password: <your redis password>
  redis_port: 1992

You can view your cost once you set up Virtual keys or custom_callbacks

Load API Keys / config values from Environment

If you have secrets saved in your environment, and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment. This works for ANY value on the config.yaml

os.environ/<YOUR-ENV-VAR> # runs os.getenv("YOUR-ENV-VAR")

model_list:
  - model_name: gpt-4-team1
    litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body
      model: azure/chatgpt-v-2
      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
      api_version: "2023-05-15"
      api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY # 👈 KEY CHANGE

See Code

s/o to @David Manouchehri for helping with this.

Load API Keys from Secret Managers (Azure Vault, etc)

Using Secret Managers with LiteLLM Proxy

Set Supported Environments for a model - production, staging, development

Use this if you want to control which model is exposed on a specific litellm environment

Supported Environments:

• production
• staging
• development

1. Set LITELLM_ENVIRONMENT="<environment>" in your environment. Can be one of production, staging or development

2. For each model set the list of supported environments in model_info.supported_environments

model_list:
 - model_name: gpt-3.5-turbo
   litellm_params:
     model: openai/gpt-3.5-turbo
     api_key: os.environ/OPENAI_API_KEY
   model_info:
     supported_environments: ["development", "production", "staging"]
 - model_name: gpt-4
   litellm_params:
     model: openai/gpt-4
     api_key: os.environ/OPENAI_API_KEY
   model_info:
     supported_environments: ["production", "staging"]
 - model_name: gpt-4o
   litellm_params:
     model: openai/gpt-4o
     api_key: os.environ/OPENAI_API_KEY
   model_info:
     supported_environments: ["production"]

Set Custom Prompt Templates

LiteLLM by default checks if a model has a prompt template and applies it (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the config.yaml:

Step 1: Save your prompt template in a config.yaml

# Model-specific parameters
model_list:
  - model_name: mistral-7b # model alias
    litellm_params: # actual params for litellm.completion()
      model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1" 
      api_base: "<your-api-base>"
      api_key: "<your-api-key>" # [OPTIONAL] for hf inference endpoints
      initial_prompt_value: "\n"
      roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}}
      final_prompt_value: "\n"
      bos_token: " "
      eos_token: " "
      max_tokens: 4096

Step 2: Start server with config

$ litellm --config /path/to/config.yaml

General Settings general_settings (DB Connection, etc)

Configure DB Pool Limits + Connection Timeouts

general_settings: 
  database_connection_pool_limit: 100 # sets connection pool for prisma client to postgres db at 100
  database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db 

All settings

environment_variables: {}

model_list:
  - model_name: string
    litellm_params: {}
    model_info:
      id: string
      mode: embedding
      input_cost_per_token: 0
      output_cost_per_token: 0
      max_tokens: 2048
      base_model: gpt-4-1106-preview
      additionalProp1: {}

litellm_settings:
  # Logging/Callback settings
  success_callback: ["langfuse"]  # list of success callbacks
  failure_callback: ["sentry"]  # list of failure callbacks
  callbacks: ["otel"]  # list of callbacks - runs on success and failure
  service_callbacks: ["datadog", "prometheus"]  # logs redis, postgres failures on datadog, prometheus
  turn_off_message_logging: boolean  # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged.
  redact_user_api_key_info: boolean  # Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging.
  langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] # default tags for Langfuse Logging
  
  
  set_verbose: boolean # sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION
  json_logs: boolean # if true, logs will be in json format

  # Fallbacks, reliability
  default_fallbacks: ["claude-opus"] # set default_fallbacks, in case a specific model group is misconfigured / bad.
  content_policy_fallbacks: [{"gpt-3.5-turbo-small": ["claude-opus"]}] # fallbacks for ContentPolicyErrors
  context_window_fallbacks: [{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}] # fallbacks for ContextWindowExceededErrors



  # Caching settings
  cache: true 
  cache_params:        # set cache params for redis
    type: redis        # type of cache to initialize

    # Optional - Redis Settings
    host: "localhost"  # The host address for the Redis cache. Required if type is "redis".
    port: 6379  # The port number for the Redis cache. Required if type is "redis".
    password: "your_password"  # The password for the Redis cache. Required if type is "redis".
    namespace: "litellm_caching" # namespace for redis cache
  
    # Optional - Redis Cluster Settings
    redis_startup_nodes: [{"host": "127.0.0.1", "port": "7001"}] 

    # Optional - Redis Sentinel Settings
    service_name: "mymaster"
    sentinel_nodes: [["localhost", 26379]]

    # Optional - Qdrant Semantic Cache Settings
    qdrant_semantic_cache_embedding_model: openai-embedding # the model should be defined on the model_list
    qdrant_collection_name: test_collection
    qdrant_quantization_config: binary
    similarity_threshold: 0.8   # similarity threshold for semantic cache

    # Optional - S3 Cache Settings
    s3_bucket_name: cache-bucket-litellm   # AWS Bucket Name for S3
    s3_region_name: us-west-2              # AWS Region Name for S3
    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY  # AWS Secret Access Key for S3
    s3_endpoint_url: https://s3.amazonaws.com  # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 bucket

    # Common Cache settings
    # Optional - Supported call types for caching
    supported_call_types: ["acompletion", "atext_completion", "aembedding", "atranscription"]
                          # /chat/completions, /completions, /embeddings, /audio/transcriptions
    mode: default_off # if default_off, you need to opt in to caching on a per call basis
    ttl: 600 # ttl for caching


callback_settings:
  otel:
    message_logging: boolean  # OTEL logging callback specific settings

general_settings:
  completion_model: string
  disable_spend_logs: boolean  # turn off writing each transaction to the db
  disable_master_key_return: boolean  # turn off returning master key on UI (checked on '/user/info' endpoint)
  disable_retry_on_max_parallel_request_limit_error: boolean  # turn off retries when max parallel request limit is reached
  disable_reset_budget: boolean  # turn off reset budget scheduled task
  disable_adding_master_key_hash_to_db: boolean  # turn off storing master key hash in db, for spend tracking
  enable_jwt_auth: boolean  # allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims
  enforce_user_param: boolean  # requires all openai endpoint requests to have a 'user' param
  allowed_routes: ["route1", "route2"]  # list of allowed proxy API routes - a user can access. (currently JWT-Auth only)
  key_management_system: google_kms  # either google_kms or azure_kms
  master_key: string
  database_url: string
  database_connection_pool_limit: 0  # default 100
  database_connection_timeout: 0  # default 60s
  custom_auth: string
  max_parallel_requests: 0  # the max parallel requests allowed per deployment 
  global_max_parallel_requests: 0  # the max parallel requests allowed on the proxy all up 
  infer_model_from_keys: true
  background_health_checks: true
  health_check_interval: 300
  alerting: ["slack", "email"]
  alerting_threshold: 0
  use_client_credentials_pass_through_routes: boolean  # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints

litellm_settings - Reference

Name	Type	Description
success_callback	array of strings	List of success callbacks. Doc Proxy logging callbacks, Doc Metrics
failure_callback	array of strings	List of failure callbacks Doc Proxy logging callbacks, Doc Metrics
callbacks	array of strings	List of callbacks - runs on success and failure Doc Proxy logging callbacks, Doc Metrics
service_callbacks	array of strings	System health monitoring - Logs redis, postgres failures on specified services (e.g. datadog, prometheus) Doc Metrics
turn_off_message_logging	boolean	If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged Proxy Logging
redact_user_api_key_info	boolean	If true, redacts information about the user api key from logs Proxy Logging
langfuse_default_tags	array of strings	Default tags for Langfuse Logging. Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields as tags. Further docs
set_verbose	boolean	If true, sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION
json_logs	boolean	If true, logs will be in json format. If you need to store the logs as JSON, just set the litellm.json_logs = True. We currently just log the raw POST request from litellm as a JSON Further docs
default_fallbacks	array of strings	List of fallback models to use if a specific model group is misconfigured / bad. Further docs
content_policy_fallbacks	array of objects	Fallbacks to use when a ContentPolicyViolationError is encountered. Further docs
context_window_fallbacks	array of objects	Fallbacks to use when a ContextWindowExceededError is encountered. Further docs
cache	boolean	If true, enables caching. Further docs
cache_params	object	Parameters for the cache. Further docs
cache_params.type	string	The type of cache to initialize. Can be one of ["local", "redis", "redis-semantic", "s3", "disk", "qdrant-semantic"]. Defaults to "redis". Furher docs
cache_params.host	string	The host address for the Redis cache. Required if type is "redis".
cache_params.port	integer	The port number for the Redis cache. Required if type is "redis".
cache_params.password	string	The password for the Redis cache. Required if type is "redis".
cache_params.namespace	string	The namespace for the Redis cache.
cache_params.redis_startup_nodes	array of objects	Redis Cluster Settings. Further docs
cache_params.service_name	string	Redis Sentinel Settings. Further docs
cache_params.sentinel_nodes	array of arrays	Redis Sentinel Settings. Further docs
cache_params.ttl	integer	The time (in seconds) to store entries in cache.
cache_params.qdrant_semantic_cache_embedding_model	string	The embedding model to use for qdrant semantic cache.
cache_params.qdrant_collection_name	string	The name of the collection to use for qdrant semantic cache.
cache_params.qdrant_quantization_config	string	The quantization configuration for the qdrant semantic cache.
cache_params.similarity_threshold	float	The similarity threshold for the semantic cache.
cache_params.s3_bucket_name	string	The name of the S3 bucket to use for the semantic cache.
cache_params.s3_region_name	string	The region name for the S3 bucket.
cache_params.s3_aws_access_key_id	string	The AWS access key ID for the S3 bucket.
cache_params.s3_aws_secret_access_key	string	The AWS secret access key for the S3 bucket.
cache_params.s3_endpoint_url	string	Optional - The endpoint URL for the S3 bucket.
cache_params.supported_call_types	array of strings	The types of calls to cache. Further docs
cache_params.mode	string	The mode of the cache. Further docs

general_settings - Reference

Name	Type	Description
completion_model	string	The default model to use for completions when model is not specified in the request
disable_spend_logs	boolean	If true, turns off writing each transaction to the database
disable_master_key_return	boolean	If true, turns off returning master key on UI. (checked on '/user/info' endpoint)
disable_retry_on_max_parallel_request_limit_error	boolean	If true, turns off retries when max parallel request limit is reached
disable_reset_budget	boolean	If true, turns off reset budget scheduled task
disable_adding_master_key_hash_to_db	boolean	If true, turns off storing master key hash in db
enable_jwt_auth	boolean	allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims. Doc on JWT Tokens
enforce_user_param	boolean	If true, requires all OpenAI endpoint requests to have a 'user' param. Doc on call hooks
allowed_routes	array of strings	List of allowed proxy API routes a user can access Doc on controlling allowed routes
key_management_system	string	Specifies the key management system. Doc Secret Managers
master_key	string	The master key for the proxy Set up Virtual Keys
database_url	string	The URL for the database connection Set up Virtual Keys
database_connection_pool_limit	integer	The limit for database connection pool Setting DB Connection Pool limit
database_connection_timeout	integer	The timeout for database connections in seconds Setting DB Connection Pool limit, timeout
custom_auth	string	Write your own custom authentication logic Doc Custom Auth
max_parallel_requests	integer	The max parallel requests allowed per deployment
global_max_parallel_requests	integer	The max parallel requests allowed on the proxy overall
infer_model_from_keys	boolean	If true, infers the model from the provided keys
background_health_checks	boolean	If true, enables background health checks. Doc on health checks
health_check_interval	integer	The interval for health checks in seconds Doc on health checks
alerting	array of strings	List of alerting methods Doc on Slack Alerting
alerting_threshold	integer	The threshold for triggering alerts Doc on Slack Alerting
use_client_credentials_pass_through_routes	boolean	If true, uses client credentials for all pass-through routes. Doc on pass through routes
health_check_details	boolean	If false, hides health check details (e.g. remaining rate limit). Doc on health checks
public_routes	List[str]	(Enterprise Feature) Control list of public routes
alert_types	List[str]	Control list of alert types to send to slack (Doc on alert types)[./alerting.md]
enforced_params	List[str]	(Enterprise Feature) List of params that must be included in all requests to the proxy
enable_oauth2_auth	boolean	(Enterprise Feature) If true, enables oauth2.0 authentication
use_x_forwarded_for	str	If true, uses the X-Forwarded-For header to get the client IP address
service_account_settings	List[Dict[str, Any]]	Set service_account_settings if you want to create settings that only apply to service account keys (Doc on service accounts)[./service_accounts.md]
image_generation_model	str	The default model to use for image generation - ignores model set in request
store_model_in_db	boolean	If true, allows /model/new endpoint to store model information in db. Endpoint disabled by default. Doc on /model/new endpoint
max_request_size_mb	int	The maximum size for requests in MB. Requests above this size will be rejected.
max_response_size_mb	int	The maximum size for responses in MB. LLM Responses above this size will not be sent.
proxy_budget_rescheduler_min_time	int	The minimum time (in seconds) to wait before checking db for budget resets.
proxy_budget_rescheduler_max_time	int	The maximum time (in seconds) to wait before checking db for budget resets.
proxy_batch_write_at	int	Time (in seconds) to wait before batch writing spend logs to the db.
alerting_args	dict	Args for Slack Alerting Doc on Slack Alerting
custom_key_generate	str	Custom function for key generation Doc on custom key generation
allowed_ips	List[str]	List of IPs allowed to access the proxy. If not set, all IPs are allowed.
embedding_model	str	The default model to use for embeddings - ignores model set in request
default_team_disabled	boolean	If true, users cannot create 'personal' keys (keys with no team_id).
alert_to_webhook_url	Dict[str]	Specify a webhook url for each alert type.
key_management_settings	List[Dict[str, Any]]	Settings for key management system (e.g. AWS KMS, Azure Key Vault) Doc on key management
allow_user_auth	boolean	(Deprecated) old approach for user authentication.
user_api_key_cache_ttl	int	The time (in seconds) to cache user api keys in memory.
disable_prisma_schema_update	boolean	If true, turns off automatic schema updates to DB
litellm_key_header_name	str	If set, allows passing LiteLLM keys as a custom header. Doc on custom headers
moderation_model	str	The default model to use for moderation.
custom_sso	str	Path to a python file that implements custom SSO logic. Doc on custom SSO
allow_client_side_credentials	boolean	If true, allows passing client side credentials to the proxy. (Useful when testing finetuning models) Doc on client side credentials
admin_only_routes	List[str]	(Enterprise Feature) List of routes that are only accessible to admin users. Doc on admin only routes
use_azure_key_vault	boolean	If true, load keys from azure key vault
use_google_kms	boolean	If true, load keys from google kms
spend_report_frequency	str	Specify how often you want a Spend Report to be sent (e.g. "1d", "2d", "30d") More on this
ui_access_mode	Literal["admin_only"]	If set, restricts access to the UI to admin users only. Docs
litellm_jwtauth	Dict[str, Any]	Settings for JWT authentication. Docs
litellm_license	str	The license key for the proxy. Docs
oauth2_config_mappings	Dict[str, str]	Define the OAuth2 config mappings
pass_through_endpoints	List[Dict[str, Any]]	Define the pass through endpoints. Docs
enable_oauth2_proxy_auth	boolean	(Enterprise Feature) If true, enables oauth2.0 authentication

router_settings - Reference

router_settings:
  routing_strategy: usage-based-routing-v2 # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
  redis_host: <your-redis-host>           # string
  redis_password: <your-redis-password>   # string
  redis_port: <your-redis-port>           # string
  enable_pre_call_check: true             # bool - Before call is made check if a call is within model context window 
  allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. 
  cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails
  disable_cooldowns: True                  # bool - Disable cooldowns for all models 
  enable_tag_filtering: True                # bool - Use tag based routing for requests
  retry_policy: {                          # Dict[str, int]: retry policy for different types of exceptions
    "AuthenticationErrorRetries": 3,
    "TimeoutErrorRetries": 3,
    "RateLimitErrorRetries": 3,
    "ContentPolicyViolationErrorRetries": 4,
    "InternalServerErrorRetries": 4
  }
  allowed_fails_policy: {
    "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment
    "AuthenticationErrorAllowedFails": 10, # int 
    "TimeoutErrorAllowedFails": 12, # int 
    "RateLimitErrorAllowedFails": 10000, # int 
    "ContentPolicyViolationErrorAllowedFails": 15, # int 
    "InternalServerErrorAllowedFails": 20, # int 
  }
  content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations
  fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors

Name	Type	Description
routing_strategy	string	The strategy used for routing requests. Options: "simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing". Default is "simple-shuffle". More information here
redis_host	string	The host address for the Redis server. Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them
redis_password	string	The password for the Redis server. Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them
redis_port	string	The port number for the Redis server. Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them
enable_pre_call_check	boolean	If true, checks if a call is within the model's context window before making the call. More information here
content_policy_fallbacks	array of objects	Specifies fallback models for content policy violations. More information here
fallbacks	array of objects	Specifies fallback models for all types of errors. More information here
enable_tag_filtering	boolean	If true, uses tag based routing for requests Tag Based Routing
cooldown_time	integer	The duration (in seconds) to cooldown a model if it exceeds the allowed failures.
disable_cooldowns	boolean	If true, disables cooldowns for all models. More information here
retry_policy	object	Specifies the number of retries for different types of exceptions. More information here
allowed_fails	integer	The number of failures allowed before cooling down a model. More information here
allowed_fails_policy	object	Specifies the number of allowed failures for different error types before cooling down a deployment. More information here

Extras

Disable Swagger UI

To disable the Swagger docs from the base url, set

NO_DOCS="True"

in your environment, and restart the proxy.

Use CONFIG_FILE_PATH for proxy (Easier Azure container deployment)

1. Setup config.yaml

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

2. Store filepath as env var

CONFIG_FILE_PATH="/path/to/config.yaml"

3. Start Proxy

$ litellm 

# RUNNING on http://0.0.0.0:4000