# Quickstart
<a name="getting-started"></a>

Get started with Amazon Bedrock in minutes. For new applications, we recommend the [`bedrock-runtime`](endpoints.md) endpoint. The following steps walk you through running your first inference request using the Anthropic-native [Messages API](inference-messages-api.md), the OpenAI-compatible [Responses API](bedrock-mantle.md) and [Chat Completions API](inference-chat-completions.md), and the [Converse](conversation-inference.md) and [Invoke](inference-invoke.md) APIs. For a complete list of APIs, see [Build](build.md). After you complete these steps, you can send inference requests to any supported foundation model.

**To run your first inference request**

1. Sign up for an [AWS account](https://portal.aws.amazon.com/billing/signup).

   If you already have an AWS account, skip this step.

1. Generate a short-term API key to authenticate your requests to Amazon Bedrock by opening the [**Amazon Bedrock** service in the AWS Management Console](https://console.aws.amazon.com/bedrock/home#/api-keys/short-term/create).

   For the complete procedure, see the [API keys](api-keys.md) section.

   For production applications, use [IAM roles or temporary credentials](https://docs.aws.amazon.com/IAM/latest/UserGuide/security-creds-programmatic-access.html#security-creds-alternatives-to-long-term-access-keys). 

1. Install the relevant SDK for the APIs you plan to use. Python must already be installed.

------
#### [ Messages API ]

   ```
   pip install anthropic aws-bedrock-token-generator
   ```

------
#### [ Responses/Chat Completions API ]

   ```
   pip install boto3 openai
   ```

------
#### [ Invoke/Converse API ]

   ```
   pip install boto3
   ```

------

1. Set the following environment variables to use the API key for authentication.

------
#### [ Messages API ]

   ```
   AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"
   ANTHROPIC_BASE_URL="https://bedrock-runtime.<your-region>.amazonaws.com/anthropic"
   ```

------
#### [ Responses/Chat Completions API ]

   ```
   OPENAI_API_KEY="<provide your Bedrock API key>"
   OPENAI_BASE_URL="https://bedrock-runtime.<your-region>.amazonaws.com/openai/v1"
   ```

------
#### [ Invoke/Converse API ]

   ```
   AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"
   ```

------

1. Choose a model and run your first inference request. 

   1. Choose a model. Amazon Bedrock supports [100\+ foundation models](models.md). 

   1. Use the following Python code to run your first inference request.

------
#### [ Messages API ]

      ```
      from anthropic import Anthropic
      from aws_bedrock_token_generator import provide_token
      
      token = provide_token(region="us-east-1")
      
      client = Anthropic(api_key=token)
      
      response = client.messages.create(
          model="global.anthropic.claude-opus-5",
          max_tokens=1024,
          messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}]
      )
      print(response)
      ```

------
#### [ Responses API ]

      ```
      from openai import OpenAI
      
      client = OpenAI()
      
      response = client.responses.create(
          model="openai.gpt-5.6-sol",
          input="Can you explain the features of Amazon Bedrock?"
          )
      print(response)
      ```

------
#### [ Chat Completions API ]

      ```
      from openai import OpenAI
      
      client = OpenAI()
      
      response = client.chat.completions.create(
          model="openai.gpt-5.6-sol",
          messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}]
          )
      print(response)
      ```

------
#### [ Converse API ]

      ```
      import boto3
      
      client = boto3.client('bedrock-runtime', region_name='us-east-1')
      response = client.converse(
          modelId='global.anthropic.claude-opus-5',
          messages=[
              {
                  'role': 'user',
                  'content': [{'text': 'Can you explain the features of Amazon Bedrock?'}]
              }
          ]
      )
      print(response)
      ```

------
#### [ Invoke API ]

      ```
      import json
      import boto3
      
      client = boto3.client('bedrock-runtime', region_name='us-east-1')
      response = client.invoke_model(
          modelId='global.anthropic.claude-opus-5',
          body=json.dumps({
                  'anthropic_version': 'bedrock-2023-05-31',
                  'messages': [{ 'role': 'user', 'content': 'Can you explain the features of Amazon Bedrock?'}],
                  'max_tokens': 1024
          })
       )
       print(json.loads(response['body'].read()))
      ```

------

   1. Save the file as `bedrock-first-request.py`.

   1. Run the code with the following command:

      ```
      python3 bedrock-first-request.py
      ```

      You should see the output of your inference request.

To learn more about using other APIs and endpoints, see [Build](build.md) and [Endpoints supported by Amazon Bedrock](endpoints.md).

## Next steps
<a name="getting-started-next-steps"></a>

Now that you have run your first request, explore the following resources to build more with Amazon Bedrock:
+ [Automate tasks in your application using AI agents](agents.md) – Create agents that can orchestrate multi-step tasks.
+ [Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases](knowledge-base.md) – Connect foundation models to your data sources.
+ [Customize your model to improve its performance for your use case](custom-models.md) – Fine-tune models for your use case.
+ [Evaluate the performance of Amazon Bedrock resources](evaluation.md) – Evaluate model performance for your workloads.

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon Bedrock. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query bedrock` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).