litellm/docs/my-website/docs/bedrock_converse.md

/converse

Call Bedrock's /converse endpoint through LiteLLM Proxy.

Feature	Supported
Cost Tracking	✅
Logging	✅
Streaming	✅ via /converse-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 /converse endpoint

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-bedrock-model/converse' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "messages": [
        {
            "role": "user",
            "content": [{"text": "Hello, how are you?"}]
        }
    ],
    "inferenceConfig": {
        "temperature": 0.5,
        "maxTokens": 100
    }
}'

Streaming

For streaming responses, use /converse-stream:

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-bedrock-model/converse-stream' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "messages": [
        {
            "role": "user",
            "content": [{"text": "Tell me a short story"}]
        }
    ],
    "inferenceConfig": {
        "temperature": 0.7,
        "maxTokens": 200
    }
}'

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.converse(
    modelId='my-bedrock-model',  # Your model_name from config.yaml
    messages=[
        {
            "role": "user",
            "content": [{"text": "Hello, how are you?"}]
        }
    ],
    inferenceConfig={
        "temperature": 0.5,
        "maxTokens": 100
    }
)

print(response['output']['message']['content'][0]['text'])

More Info

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

• Full Bedrock Passthrough Docs