---
title: "/converse"
url: "/docs/bedrock_converse"
canonical_url: "https://docs.litellm.ai/docs/bedrock_converse"
type: "docs"
last_updated: "2026-10-10"
summary: "Call Bedrock's /converse endpoint through LiteLLM Proxy."
related:
  - "/docs/completion/http_handler_config"
  - "/docs/files_endpoints"
---
# /converse

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


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

```yaml showLineNumbers
model_list:
  - model_name: my-bedrock-model
    litellm_params:
      model: bedrock/us.anthropic.claude-sonnet-5
      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:

```bash showLineNumbers
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
```

### 2. Start Proxy

```bash showLineNumbers
litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000
```

### 3. Call /converse endpoint

```bash showLineNumbers
curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-bedrock-model/converse' \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-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`:

```bash showLineNumbers
curl -X POST 'http://0.0.0.0:4000/bedrock/model/my-bedrock-model/converse-stream' \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-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:

```yaml showLineNumbers
model_list:
  # Deployment 1 - us-west-2
  - model_name: my-bedrock-model
    litellm_params:
      model: bedrock/us.anthropic.claude-sonnet-5
      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-sonnet-5
      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

```python showLineNumbers
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-<your-litellm-api-key>"  # 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](./pass_through/bedrock)

## Related pages

- [Custom HTTP Handler](https://docs.litellm.ai/docs/completion/http_handler_config.md)
- [Provider Files Endpoints](https://docs.litellm.ai/docs/files_endpoints.md)