Quick Start

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Overview

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

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-4o will load balance between azure/gpt-4o-eu and azure/gpt-4o-ca

model_list:
  - model_name: gpt-4o ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: azure/gpt-4o-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-4o
    litellm_params:
      model: azure/gpt-4o-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-4o on config.yaml.

If multiple with model_name=gpt-4o 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-4o",
      "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-4o
    litellm_params:
      model: openai/gpt-4o
      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

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

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 ''

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-4o
    litellm_params:
        model: gpt-4o
        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-4o"]}] # fallback to gpt-4o if call fails num_retries 
  context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-4o": ["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-4o"} # all requests with `gpt-4` will be routed to models with `gpt-4o`
  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.

Centralized Credential Management

Define credentials once and reuse them across multiple models. This helps with:

• Secret rotation
• Reducing config duplication

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: azure/gpt-4o
      litellm_credential_name: default_azure_credential  # Reference credential below

credential_list:
  - credential_name: default_azure_credential
    credential_values:
      api_key: os.environ/AZURE_API_KEY  # Load from environment
      api_base: os.environ/AZURE_API_BASE
      api_version: "2023-05-15"
    credential_info:
      description: "Production credentials for EU region"
      custom_llm_provider: "azure"

Key Parameters

• credential_name: Unique identifier for the credential set
• credential_values: Key-value pairs of credentials/secrets (supports os.environ/ syntax)
• credential_info: Key-value pairs of user provided credentials information. No key-value pairs are required, but the dictionary must exist.

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-16k
   litellm_params:
     model: openai/gpt-3.5-turbo-16k
     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

Set custom tokenizer

If you're using the /utils/token_counter endpoint, and want to set a custom huggingface tokenizer for a model, you can do so in the config.yaml

model_list:
  - model_name: openai-deepseek
    litellm_params:
      model: deepseek/deepseek-chat
      api_key: os.environ/OPENAI_API_KEY
    model_info:
      access_groups: ["restricted-models"]
      custom_tokenizer: 
        identifier: deepseek-ai/DeepSeek-V3-Base
        revision: main
        auth_token: os.environ/HUGGINGFACE_API_KEY

Spec

custom_tokenizer: 
  identifier: str # huggingface model identifier
  revision: str # huggingface model revision (usually 'main')
  auth_token: Optional[str] # huggingface auth token 

General Settings general_settings (DB Connection, etc)

Configure DB Pool Limits + Connection Timeouts

general_settings: 
  database_connection_pool_limit: 10 # sets connection pool per worker for prisma client to postgres db (default: 10, recommended: 10-20)
  database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db 

How to calculate the right value:

The connection limit is applied per worker process, not per instance. This means if you have multiple workers, each worker will create its own connection pool.

Formula:

database_connection_pool_limit = MAX_DB_CONNECTIONS ÷ (number_of_instances × number_of_workers_per_instance)

Example:

• Your database allows a maximum of 100 connections
• You're running 1 instance of LiteLLM
• Each instance has 8 workers (set via --num_workers 8)

Calculation: 100 ÷ (1 × 8) = 12.5

Since you shouldn't use 12.5, round down to 10 to leave a safety buffer. This means:

• Each of the 8 workers will have a connection pool limit of 10
• Total maximum connections: 8 workers × 10 connections = 80 connections
• This stays safely under your database's 100 connection limit

Extras

Disable Swagger UI

To disable the Swagger docs from the base url, set

NO_DOCS="True"

in your environment, and restart the proxy.

Disable Redoc

To disable the Redoc docs (defaults to <your-proxy-url>/redoc), set

NO_REDOC="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-4o
    litellm_params:
      model: gpt-4o
      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

Providing LiteLLM config.yaml file as a s3, GCS Bucket Object/url

Use this if you cannot mount a config file on your deployment service (example - AWS Fargate, Railway etc)

LiteLLM Proxy will read your config.yaml from an s3 Bucket or GCS Bucket

Set the following .env vars

LITELLM_CONFIG_BUCKET_TYPE = "gcs"                              # set this to "gcs"         
LITELLM_CONFIG_BUCKET_NAME = "litellm-proxy"                    # your bucket name on GCS
LITELLM_CONFIG_BUCKET_OBJECT_KEY = "proxy_config.yaml"         # object key on GCS

Start litellm proxy with these env vars - litellm will read your config from GCS

docker run --name litellm-proxy \
   -e DATABASE_URL=<database_url> \
   -e LITELLM_CONFIG_BUCKET_NAME=<bucket_name> \
   -e LITELLM_CONFIG_BUCKET_OBJECT_KEY="<object_key>> \
   -e LITELLM_CONFIG_BUCKET_TYPE="gcs" \
   -p 4000:4000 \
   docker.litellm.ai/berriai/litellm-database:main-latest --detailed_debug

Set the following .env vars

LITELLM_CONFIG_BUCKET_NAME = "litellm-proxy"                    # your bucket name on s3 
LITELLM_CONFIG_BUCKET_OBJECT_KEY = "litellm_proxy_config.yaml"  # object key on s3

Start litellm proxy with these env vars - litellm will read your config from s3

docker run --name litellm-proxy \
   -e DATABASE_URL=<database_url> \
   -e LITELLM_CONFIG_BUCKET_NAME=<bucket_name> \
   -e LITELLM_CONFIG_BUCKET_OBJECT_KEY="<object_key>> \
   -p 4000:4000 \
   docker.litellm.ai/berriai/litellm-database:main-latest