Bedrock (boto3) SDK

Pass-through endpoints for Bedrock - call provider-specific endpoint, in native format (no translation).

Feature	Supported	Notes
Cost Tracking	✅	For `/invoke` and `/converse` endpoints
Load Balancing	✅	You can load balance `/invoke`, `/converse` routes across multiple deployments
End-user Tracking	❌	Tell us if you need this
Streaming	✅

Just replace https://bedrock-runtime.{aws_region_name}.amazonaws.com with LITELLM_PROXY_BASE_URL/bedrock 🚀

Overview

LiteLLM supports two ways to call Bedrock endpoints:

1. Using config.yaml (Recommended for model endpoints)

Define your Bedrock models in config.yaml and reference them by name. The proxy handles authentication and routing.

Use for: /converse, /converse-stream, /invoke, /invoke-with-response-stream

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
      custom_llm_provider: bedrock

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"}]}]}'

2. Direct passthrough (For non-model endpoints)

Set AWS credentials via environment variables and call Bedrock endpoints directly.

Use for: Guardrails, Knowledge Bases, Agents, and other non-model endpoints

export AWS_ACCESS_KEY_ID=""
export AWS_SECRET_ACCESS_KEY=""
export AWS_REGION_NAME="us-west-2"

curl "http://0.0.0.0:4000/bedrock/guardrail/my-guardrail-id/version/1/apply" \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{"contents": [{"text": {"text": "Hello"}}], "source": "INPUT"}'

Supports ALL Bedrock Endpoints (including streaming).

See All Bedrock Endpoints

Quick Start

Let's call the Bedrock /converse endpoint

Create a config.yaml file with your Bedrock model

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
      custom_llm_provider: bedrock

Set your AWS credentials:

export AWS_ACCESS_KEY_ID=""  # Access key
export AWS_SECRET_ACCESS_KEY="" # Secret access key

Start LiteLLM Proxy

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

Test it!

Let's call the Bedrock converse endpoint using the model name from config:

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": {
        "maxTokens": 100
    }
}'

Setup with config.yaml

Use config.yaml to define Bedrock models and use them via passthrough endpoints.

1. Define models in config.yaml

model_list:
  - model_name: my-claude-model
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-west-2
      custom_llm_provider: bedrock
  
  - model_name: my-cohere-model
    litellm_params:
      model: bedrock/cohere.command-r-v1:0
      aws_region_name: us-east-1
      custom_llm_provider: bedrock

2. Start proxy with config

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

3. Call Bedrock Converse endpoint

Use the model_name from config in the URL path:

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-claude-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
    }
}'

4. Call Bedrock Converse Stream endpoint

For streaming responses, use the /converse-stream endpoint:

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-claude-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
    }
}'

Supported Bedrock Endpoints with config.yaml

When using models from config.yaml, you can call any Bedrock endpoint:

Endpoint	Description	Example
`/model/{model_name}/converse`	Converse API	`http://0.0.0.0:4000/bedrock/model/my-claude-model/converse`
`/model/{model_name}/converse-stream`	Streaming Converse	`http://0.0.0.0:4000/bedrock/model/my-claude-model/converse-stream`
`/model/{model_name}/invoke`	Legacy Invoke API	`http://0.0.0.0:4000/bedrock/model/my-claude-model/invoke`
`/model/{model_name}/invoke-with-response-stream`	Legacy Streaming	`http://0.0.0.0:4000/bedrock/model/my-claude-model/invoke-with-response-stream`

The proxy automatically resolves the model_name to the actual Bedrock model ID and region configured in your config.yaml.

Load Balancing Across Multiple Deployments

Define multiple Bedrock deployments with the same model_name to enable automatic load balancing.

1. Define multiple deployments in config.yaml

model_list:
  # First deployment - us-west-2
  - model_name: my-claude-model
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-west-2
      custom_llm_provider: bedrock
  
  # Second deployment - us-east-1 (load balanced)
  - model_name: my-claude-model
    litellm_params:
      model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-east-1
      custom_llm_provider: bedrock

2. Start proxy with config

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

3. Call the endpoint - requests are automatically load balanced

curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-claude-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"
}'

The proxy will automatically distribute requests across both us-west-2 and us-east-1 deployments. This works for all Bedrock endpoints: /invoke, /invoke-with-response-stream, /converse, and /converse-stream.

Using boto3 SDK with load balancing

You can also call the load-balanced endpoint using the 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'
)

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

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

The proxy will automatically load balance your boto3 requests across all configured deployments.

Examples

Anything after http://0.0.0.0:4000/bedrock is treated as a provider-specific route, and handled accordingly.

Key Changes:

Original Endpoint	Replace With
`https://bedrock-runtime.{aws_region_name}.amazonaws.com`	`http://0.0.0.0:4000/bedrock` (LITELLM_PROXY_BASE_URL="http://0.0.0.0:4000")
`AWS4-HMAC-SHA256..`	`Bearer anything` (use `Bearer LITELLM_VIRTUAL_KEY` if Virtual Keys are setup on proxy)

Example 1: Converse API

LiteLLM Proxy Call

curl -X POST 'http://0.0.0.0:4000/bedrock/model/cohere.command-r-v1:0/converse' \
-H 'Authorization: Bearer sk-anything' \
-H 'Content-Type: application/json' \
-d '{
    "messages": [
         {"role": "user",
        "content": [{"text": "Hello"}]
    }
    ]
}'

Direct Bedrock API Call

curl -X POST 'https://bedrock-runtime.us-west-2.amazonaws.com/model/cohere.command-r-v1:0/converse' \
-H 'Authorization: AWS4-HMAC-SHA256..' \
-H 'Content-Type: application/json' \
-d '{
    "messages": [
         {"role": "user",
        "content": [{"text": "Hello"}]
    }
    ]
}'

Example 2: Apply Guardrail

Setup: Set AWS credentials for direct passthrough

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION_NAME="us-west-2"

Start proxy:

litellm

# RUNNING on http://0.0.0.0:4000

LiteLLM Proxy Call

curl "http://0.0.0.0:4000/bedrock/guardrail/guardrailIdentifier/version/guardrailVersion/apply" \
    -H 'Authorization: Bearer sk-anything' \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"text": {"text": "Hello world"}}],
      "source": "INPUT"
       }'

Direct Bedrock API Call

curl "https://bedrock-runtime.us-west-2.amazonaws.com/guardrail/guardrailIdentifier/version/guardrailVersion/apply" \
    -H 'Authorization: AWS4-HMAC-SHA256..' \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{"text": {"text": "Hello world"}}],
      "source": "INPUT"
       }'

Example 3: Query Knowledge Base

Setup: Set AWS credentials for direct passthrough

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION_NAME="us-west-2"

Start proxy:

litellm

# RUNNING on http://0.0.0.0:4000

LiteLLM Proxy Call

curl -X POST "http://0.0.0.0:4000/bedrock/knowledgebases/{knowledgeBaseId}/retrieve" \
-H 'Authorization: Bearer sk-anything' \
-H 'Content-Type: application/json' \
-d '{
    "nextToken": "string",
    "retrievalConfiguration": { 
        "vectorSearchConfiguration": { 
          "filter": { ... },
          "numberOfResults": number,
          "overrideSearchType": "string"
        }
    },
    "retrievalQuery": { 
        "text": "string"
    }
}'

Direct Bedrock API Call

curl -X POST "https://bedrock-agent-runtime.us-west-2.amazonaws.com/knowledgebases/{knowledgeBaseId}/retrieve" \
-H 'Authorization: AWS4-HMAC-SHA256..' \
-H 'Content-Type: application/json' \
-d '{
    "nextToken": "string",
    "retrievalConfiguration": { 
        "vectorSearchConfiguration": { 
          "filter": { ... },
          "numberOfResults": number,
          "overrideSearchType": "string"
        }
    },
    "retrievalQuery": { 
        "text": "string"
    }
}'

Advanced - Use with Virtual Keys

Pre-requisites

Setup proxy with DB

Use this, to avoid giving developers the raw AWS Keys, but still letting them use AWS Bedrock endpoints.

Usage

Setup environment

export DATABASE_URL=""
export LITELLM_MASTER_KEY=""
export AWS_ACCESS_KEY_ID=""  # Access key
export AWS_SECRET_ACCESS_KEY="" # Secret access key
export AWS_REGION_NAME="" # us-east-1, us-east-2, us-west-1, us-west-2

litellm

# RUNNING on http://0.0.0.0:4000

Generate virtual key

curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{}'

Expected Response

{
    ...
    "key": "sk-1234ewknldferwedojwojw"
}

Test it!

curl -X POST 'http://0.0.0.0:4000/bedrock/model/cohere.command-r-v1:0/converse' \
-H 'Authorization: Bearer sk-1234ewknldferwedojwojw' \
-H 'Content-Type: application/json' \
-d '{
    "messages": [
         {"role": "user",
        "content": [{"text": "Hello"}]
    }
    ]
}'

Advanced - Bedrock Agents

Call Bedrock Agents via LiteLLM proxy

Setup: Set AWS credentials on your LiteLLM proxy server

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION_NAME="us-west-2"

Start proxy:

litellm

# RUNNING on http://0.0.0.0:4000

Usage from Python:

import os 
import boto3

# 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

# Create the client
runtime_client = boto3.client(
    service_name="bedrock-agent-runtime", 
    region_name="us-west-2", 
    endpoint_url="http://0.0.0.0:4000/bedrock"
)

response = runtime_client.invoke_agent(
    agentId="L1RT58GYRW",
    agentAliasId="MFPSBCXYTW",
    sessionId="12345",
    inputText="Who do you know?"
)

completion = ""

for event in response.get("completion"):
    chunk = event["chunk"]
    completion += chunk["bytes"].decode()

print(completion)

Using LangChain AWS SDK with LiteLLM

You can use the LangChain AWS SDK with LiteLLM Proxy to get cost tracking, load balancing, and other LiteLLM features.

Quick Start

1. Install LangChain AWS:

uv add langchain-aws

2. Setup LiteLLM Proxy:

Create a config.yaml:

model_list:
  - model_name: claude-sonnet
    litellm_params:
      model: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0
      aws_region_name: us-east-1
      custom_llm_provider: bedrock

Start the proxy:

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

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

3. Use LangChain with LiteLLM:

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

# Your LiteLLM API key
API_KEY = "Bearer sk-1234"

# Initialize ChatBedrockConverse pointing to LiteLLM proxy
llm = ChatBedrockConverse(
    model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
    endpoint_url="http://localhost:4000/bedrock",
    region_name="us-east-1",
    aws_access_key_id=API_KEY,
    aws_secret_access_key="bedrock"  # Any non-empty value works
)

# Invoke the model
messages = [HumanMessage(content="Hello, how are you?")]
response = llm.invoke(messages)

print(response.content)

Advanced Example: PDF Document Processing with Citations

LangChain AWS SDK supports Bedrock's document processing features. Here's how to use it with LiteLLM:

import os
import json
from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

# Your LiteLLM API key
API_KEY = "Bearer sk-1234"

def get_llm() -> ChatBedrockConverse:
    """Initialize LLM pointing to LiteLLM proxy"""
    llm = ChatBedrockConverse(
        model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
        base_model_id="anthropic.claude-3-7-sonnet-20250219-v1:0",
        endpoint_url="http://localhost:4000/bedrock",
        region_name="us-east-1",
        aws_access_key_id=API_KEY,
        aws_secret_access_key="bedrock"
    )
    return llm

if __name__ == "__main__":
    # Initialize the LLM
    llm = get_llm()
    
    # Read PDF file as bytes (Converse API requires raw bytes)
    with open("your-document.pdf", "rb") as file:
        file_bytes = file.read()
    
    # Prepare messages with document attachment
    messages = [
        HumanMessage(content=[
            {"text": "What is the policy number in this document?"},
            {
                "document": {
                    "format": "pdf",
                    "name": "PolicyDocument",
                    "source": {"bytes": file_bytes},
                    "citations": {"enabled": True}
                }
            }
        ])
    ]
    
    # Invoke the LLM
    response = llm.invoke(messages)
    
    # Print response with citations
    print(json.dumps(response.content, indent=4))

Supported LangChain Features

All LangChain AWS features work with LiteLLM:

Feature	Supported	Notes
Text Generation	✅	Full support
Streaming	✅	Use `stream()` method
Document Processing	✅	PDF, images, etc.
Citations	✅	Enable in document config
Tool Use	✅	Function calling support
Multi-modal	✅	Text + images + documents

Troubleshooting

Issue: UnknownOperationException error

Solution: Make sure you're using the correct endpoint URL format:

✅ Correct: http://localhost:4000/bedrock
❌ Wrong: http://localhost:4000/bedrock/v2

Issue: Authentication errors

Solution: Ensure your API key is in the correct format:

aws_access_key_id="Bearer sk-1234"  # Include "Bearer " prefix

Overview​

1. Using config.yaml (Recommended for model endpoints)​

2. Direct passthrough (For non-model endpoints)​

Quick Start​

Setup with config.yaml​

1. Define models in config.yaml​

2. Start proxy with config​

3. Call Bedrock Converse endpoint​

4. Call Bedrock Converse Stream endpoint​

Supported Bedrock Endpoints with config.yaml​

Load Balancing Across Multiple Deployments​

1. Define multiple deployments in config.yaml​

2. Start proxy with config​

3. Call the endpoint - requests are automatically load balanced​

Using boto3 SDK with load balancing​

Examples​

Example 1: Converse API​

LiteLLM Proxy Call​

Direct Bedrock API Call​

Example 2: Apply Guardrail​

LiteLLM Proxy Call​

Direct Bedrock API Call​

Example 3: Query Knowledge Base​

LiteLLM Proxy Call​

Direct Bedrock API Call​

Advanced - Use with Virtual Keys​

Usage​

Advanced - Bedrock Agents​

Using LangChain AWS SDK with LiteLLM​

Quick Start​

Advanced Example: PDF Document Processing with Citations​

Supported LangChain Features​

Troubleshooting​

Overview

1. Using config.yaml (Recommended for model endpoints)

2. Direct passthrough (For non-model endpoints)

Quick Start

Setup with config.yaml

1. Define models in config.yaml

2. Start proxy with config

3. Call Bedrock Converse endpoint

4. Call Bedrock Converse Stream endpoint

Supported Bedrock Endpoints with config.yaml

Load Balancing Across Multiple Deployments

1. Define multiple deployments in config.yaml

2. Start proxy with config

3. Call the endpoint - requests are automatically load balanced

Using boto3 SDK with load balancing

Examples

Example 1: Converse API

LiteLLM Proxy Call

Direct Bedrock API Call

Example 2: Apply Guardrail

LiteLLM Proxy Call

Direct Bedrock API Call

Example 3: Query Knowledge Base

LiteLLM Proxy Call

Direct Bedrock API Call

Advanced - Use with Virtual Keys

Usage

Advanced - Bedrock Agents

Using LangChain AWS SDK with LiteLLM

Quick Start

Advanced Example: PDF Document Processing with Citations

Supported LangChain Features

Troubleshooting