litellm-mirror/docs/my-website/docs/pass_through/bedrock.md

Bedrock (boto3) SDK

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

Feature	Supported	Notes
Cost Tracking	❌	Tell us if you need this
Logging	✅	works across all integrations
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 🚀

Example Usage

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

Supports ALL Bedrock Endpoints (including streaming).

See All Bedrock Endpoints

Quick Start

Let's call the Bedrock /converse endpoint

1. Add AWS Keyss to your environment

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

2. Start LiteLLM Proxy

litellm

# RUNNING on http://0.0.0.0:4000

3. Test it!

Let's call the Bedrock converse endpoint

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

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

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

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

1. 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

2. 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"
}

3. 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

import os 
import boto3 
from botocore.config import Config

# # Define your proxy endpoint
proxy_endpoint = "http://0.0.0.0:4000/bedrock" # 👈 your proxy base url

# # Create a Config object with the proxy
# Custom headers
custom_headers = {
    'litellm_user_api_key': 'sk-1234', # 👈 your proxy api key
}


os.environ["AWS_ACCESS_KEY_ID"] = "my-fake-key-id"
os.environ["AWS_SECRET_ACCESS_KEY"] = "my-fake-access-key"


# Create the client
runtime_client = boto3.client(
    service_name="bedrock-agent-runtime", 
    region_name="us-west-2", 
    endpoint_url=proxy_endpoint
)

# Custom header injection
def inject_custom_headers(request, **kwargs):
    request.headers.update({
        'litellm_user_api_key': 'sk-1234',
    })

# Attach the event to inject custom headers before the request is sent
runtime_client.meta.events.register('before-send.*.*', inject_custom_headers)


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)