Vibe Coding with Amazon Bedrock Claude Opus 4.7 with LiteLLM and VSCode RooCode

Amazon Web Services has introduced Claude Opus 4.7 via Amazon Bedrock, bringing one of the most powerful AI models into a managed, enterprise-ready environment. While AWS provides native SDK integrations, many developers prefer a simpler and more flexible setup.

This guide shows how to use Claude Opus 4.7 with:

• LiteLLM (OpenAI-compatible proxy)
• VSCode RooCode extension

The result is a streamlined workflow that avoids unnecessary complexity while retaining full access to Bedrock’s capabilities.

What is Claude Opus 4.7?

Claude Opus 4.7 is Anthropic’s most advanced model available on Amazon Bedrock. It is designed for:

• Advanced reasoning and problem-solving
• Code generation and debugging
• Long-context processing (up to ~1M tokens)
• Agent-based workflows and automation

This makes it particularly useful for developers building intelligent tools, assistants, or backend systems.

Why Use LiteLLM?

Amazon Bedrock requires AWS-specific APIs and authentication. LiteLLM simplifies this by acting as a proxy that converts requests into an OpenAI-compatible format.

1. One API instead of five personalities

Every provider thinks they’re special:

• OpenAI → one format
• Anthropic → different format
• Amazon Web Services Bedrock → completely different API

LiteLLM normalizes all of this into a single interface.

So instead of rewriting code per provide, you swap model names.

That’s it.

2. Works with OpenAI-compatible tools

Most tooling assumes OpenAI APIs:

• VSCode extensions
• agents
• SDKs
• random GitHub projects built at 2 AM

LiteLLM pretends to be OpenAI:

http://localhost:4000/v1

Everything just works without modification.

3. Provider switching without drama

Want to switch from:

• OpenAI → Claude
• Claude → Bedrock
• Bedrock → something cheaper

Without LiteLLM:

• rewrite logic
• change payload formats
• test everything again

With LiteLLM:

model: openai/gpt-4o
# becomes
model: bedrock/anthropic.claude-opus-4-7

That’s your “migration plan.”

4. Centralized config (less chaos)

Instead of config scattered across:

• env files
• SDK calls
• random helper functions

You define models in one place:

model_list: 
   - model_name: production-model 
     litellm_params: 
        model: bedrock/anthropic.claude-opus-4-7

5. Built-in proxy = instant API layer

Run:

litellm --port 4000

Now you get:

• logging
• routing
• retries
• unified endpoint

Without building your own backend.

6. Cost and routing control

You can:

• route requests to cheaper models
• fallback if one fails
• split traffic

Which is great if you enjoy not burning money unnecessarily

7. Makes Bedrock usable (honestly)

Amazon Web Services Bedrock is powerful, but:

• verbose APIs
• strict auth
• provider-specific payloads

LiteLLM hides all that behind a normal interface.

Suddenly Bedrock feels like a modern API instead of a compliance exercise

Architecture Overview

The setup looks like this:

VSCode (RooCode)
        ↓
LiteLLM Proxy (OpenAI-compatible API)
        ↓
Amazon Bedrock (Claude Opus 4.7)

LiteLLM acts as the bridge between developer tools and AWS Bedrock.

Step 1: Enable Claude Opus 4.7 in Bedrock

1. Open the Amazon Bedrock console.
2. Navigate to Model Access
3. Request access to Claude Opus 4.7

Approval may take some time depending on your account.

To quickly make your first API call to Amazon Bedrock, navigate to the Quickstart section from the left panel in the console. Based on your selected use case, you can generate a short-lived API key for testing and authentication.

After selecting an API option, such as the OpenAI-compatible Responses API, you’ll be provided with sample code that allows you to run prompts and send inference requests using the chosen model.

Step 2: Configure AWS Credentials

Install and configure AWS CLI:

aws configure

Provide:

• AWS Access Key ID
• AWS Secret Access Key
• Region (e.g., us-east-1)

Step 3: Install LiteLLM

pip install litellm

Step 4: Configure LiteLLM for Bedrock

You can configure LiteLLM using environment variables:

export AWS_ACCESS_KEY_ID=your_key 
export AWS_SECRET_ACCESS_KEY=your_secret 
export AWS_REGION_NAME=us-east-1

Or define a config file:

 ~/.litellm/config.yaml

model_list:
  - model_name: claude-opus-4-7
    litellm_params:
      model: bedrock/anthropic.claude-opus-4-7

Here is how LiteLLM works behind the scene:

1. Accepts your request (OpenAI format)
2. Converts it into a Bedrock API call
3. Signs it using AWS credentials
4. Sends it to Bedrock
5. Translates the response back

So your app thinks it’s talking to OpenAI, while AWS quietly handles the actual work.

Step 5: Start LiteLLM Proxy

Run:

litellm --config ~/.litellm/config.yaml --port 4000

This exposes an OpenAI-compatible API at:

http://localhost:4000/v1

Step 6: Configure RooCode in VSCode

Download RooCode extension here 

{
  "apiBase": "http://localhost:4000/v1",
  "apiKey": "any-value",
  "model": "claude-opus-4-7"
}

LiteLLM handles authentication, so the API key value can be arbitrary.

Step 7: Test the Setup

You can test using a simple API request:

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-7",
    "messages": <span class="text-token-text-primary cursor-text rounded-sm" data-placeholder-token="true">[
      {"role": "user", "content": "Write a Python FastAPI app"}
    ]</span>
  }'

If configured correctly, Claude Opus 4.7 will respond through LiteLLM.

Alternatively, you can also configure directly with AWS Credentials

Important Considerations

Model Naming

Use the correct Bedrock model ID:

anthropic.claude-opus-4-7

Region vs Global Endpoints

• Regional endpoints provide control but may cost more
• Global endpoints offer automatic routing and lower cost

Request Limits

• Maximum payload size: ~20MB
• Large inputs may hit size limits before token limits

When Should You Use Claude Opus 4.7?

Claude Opus 4.7 is best suited for:

• Complex coding tasks
• Long-running AI agents
• Deep reasoning workflows
• Enterprise-grade AI applications

For simple chatbot use cases, smaller models may be more cost-effective.

Conclusion

By combining Amazon Bedrock with LiteLLM and RooCode, developers can access powerful AI models like Claude Opus 4.7 without dealing with complex AWS integrations.

This approach:

• Simplifies development workflows
• Maintains flexibility
• Enables rapid experimentation inside familiar tools like VSCode

It’s an efficient way to bring enterprise AI capabilities into everyday development environments.

References:

https://docs.roocode.com/providers/litellm

https://aws.amazon.com/bedrock/anthropic/