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/invoke

Call Bedrock's /invoke endpoint through LiteLLM Proxy.

FeatureSupported
Cost Tracking✅
Logging✅
Streaming✅ via /invoke-with-response-stream
Load Balancing✅

Quick Start​

1. Setup config.yaml​

model_list:
  - model_name: my-bedrock-model
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-west-2
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # reads from environment
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      custom_llm_provider: bedrock

Set AWS credentials in your environment:

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"

2. Start Proxy​

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

3. Call /invoke endpoint​

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-bedrock-model/invoke' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "max_tokens": 100,
    "messages": [
        {
            "role": "user",
            "content": "Hello, how are you?"
        }
    ],
    "anthropic_version": "bedrock-2023-05-31"
}'

Streaming​

For streaming responses, use /invoke-with-response-stream:

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-bedrock-model/invoke-with-response-stream' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "max_tokens": 100,
    "messages": [
        {
            "role": "user",
            "content": "Tell me a short story"
        }
    ],
    "anthropic_version": "bedrock-2023-05-31"
}'

Load Balancing​

Define multiple deployments with the same model_name for automatic load balancing:

model_list:
  # Deployment 1 - us-west-2
  - model_name: my-bedrock-model
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-west-2
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      custom_llm_provider: bedrock
  
  # Deployment 2 - us-east-1
  - model_name: my-bedrock-model
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-east-1
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      custom_llm_provider: bedrock

The proxy automatically distributes requests across both regions.

Using boto3 SDK​

import boto3
import json
import os

# Set dummy AWS credentials (required by boto3, but not used by LiteLLM proxy)
os.environ['AWS_ACCESS_KEY_ID'] = 'dummy'
os.environ['AWS_SECRET_ACCESS_KEY'] = 'dummy'
os.environ['AWS_BEARER_TOKEN_BEDROCK'] = "sk-1234"  # your litellm proxy api key

# Point boto3 to the LiteLLM proxy
bedrock_runtime = boto3.client(
    service_name='bedrock-runtime',
    region_name='us-west-2',
    endpoint_url='http://0.0.0.0:4000/bedrock'
)

response = bedrock_runtime.invoke_model(
    modelId='my-bedrock-model',  # Your model_name from config.yaml
    contentType='application/json',
    accept='application/json',
    body=json.dumps({
        "max_tokens": 100,
        "messages": [{"role": "user", "content": "Hello"}],
        "anthropic_version": "bedrock-2023-05-31"
    })
)

response_body = json.loads(response['body'].read())
print(response_body['content'][0]['text'])

More Info​

For complete documentation including Guardrails, Knowledge Bases, and Agents, see: