promptfoo--promptfoo/examples/amazon-bedrock/models

amazon-bedrock/models (Amazon Bedrock Examples)

You can run this example with:

npx promptfoo@latest init --example amazon-bedrock/models
cd amazon-bedrock/models

Prerequisites

1. Set up your AWS credentials:

   export AWS_ACCESS_KEY_ID="your_access_key"
   export AWS_SECRET_ACCESS_KEY="your_secret_key"
   

   See authentication docs for other auth methods, including SSO profiles.

2. Request model access in your AWS region:

   • Visit the AWS Bedrock Model Access page
   • Switch to your desired region. We recommend us-west-2 and us-east-1 which tend to have the most models available.
   • Enable the models you want to use.

3. Install required dependencies:

   # For basic Bedrock models
   npm install @aws-sdk/client-bedrock-runtime
   
   # For Knowledge Base examples
   npm install @aws-sdk/client-bedrock-agent-runtime
   

Available Examples

This directory contains several example configurations for different Bedrock models:

• promptfooconfig.claude.yaml - Claude 4.6 Opus, Claude 4.1 Opus, Claude 4 Opus/Sonnet, Claude Haiku 4.5
• promptfooconfig.openai.yaml - OpenAI GPT-OSS models (120B and 20B) with reasoning effort
• promptfooconfig.openai-frontier.yaml - OpenAI frontier models (GPT-5.5 and GPT-5.4) with native reasoning effort
• promptfooconfig.grok.yaml - xAI Grok 4.3 on the Bedrock Mantle endpoint (requires AWS_BEARER_TOKEN_BEDROCK)
• promptfooconfig.mantle.yaml - bedrock:mantle: Chat Completions endpoint for mantle-only models like GLM 4.6 and DeepSeek V3.1 (requires AWS_BEARER_TOKEN_BEDROCK)
• promptfooconfig.llama.yaml - Llama3
• promptfooconfig.mistral.yaml - Mistral
• promptfooconfig.openai-compatible.yaml - OpenAI-compatible families: Z.AI GLM, MiniMax, Moonshot Kimi, NVIDIA Nemotron, Google Gemma, Writer Palmyra
• promptfooconfig.nova.yaml - Amazon's Nova models
• promptfooconfig.nova.tool.yaml - Nova with tool usage examples
• promptfooconfig.nova.multimodal.yaml - Nova with multimodal capabilities
• promptfooconfig.kb.yaml - Knowledge Base RAG example with citations and contextTransform
• promptfooconfig.inference-profiles.yaml - Comprehensive Application Inference Profiles example with multiple model types
• promptfooconfig.inference-profiles-simple.yaml - Simple production-ready inference profile setup for high availability
• promptfooconfig.yaml - Combined evaluation across multiple providers
• promptfooconfig.nova-sonic.yaml - Amazon Nova Sonic model for audio
• promptfooconfig.converse.yaml - Converse API with extended thinking (ultrathink)
• promptfooconfig.converse-mcp.yaml - Converse API with Model Context Protocol (MCP) tools

Converse API Example

The Converse API example (promptfooconfig.converse.yaml) demonstrates the unified Bedrock Converse API with extended thinking (ultrathink) support.

Key Features

• Extended Thinking: Enable Claude's reasoning capabilities with configurable token budgets
• Unified Interface: Single API format works across Claude, Nova, Llama, Mistral, and more
• Show/Hide Thinking: Control whether thinking content appears in output with showThinking

Configuration

providers:
  - id: bedrock:converse:us.anthropic.claude-sonnet-4-6
    label: Claude Sonnet 4.6 with Thinking
    config:
      region: us-west-2
      maxTokens: 20000
      thinking:
        type: enabled
        budget_tokens: 16000
      showThinking: true

Run the Converse API example with:

promptfoo eval -c examples/amazon-bedrock/models/promptfooconfig.converse.yaml

Converse MCP Example

The Converse MCP example (promptfooconfig.converse-mcp.yaml) demonstrates how to attach Model Context Protocol (MCP) servers to a Bedrock Converse provider. MCP tools are discovered from the configured server, converted to Bedrock Converse tool definitions, and executed when the model requests a tool call.

Configuration

providers:
  - id: bedrock:converse:us.anthropic.claude-sonnet-4-6
    label: Claude Sonnet 4.6 with MCP
    config:
      region: us-east-1
      maxTokens: 1024
      temperature: 0
      mcp:
        enabled: true
        servers:
          - name: deepwiki
            url: https://mcp.deepwiki.com/mcp
        tools:
          - ask_question
      toolChoice: auto

Run the Converse MCP example with:

promptfoo eval -c examples/amazon-bedrock/models/promptfooconfig.converse-mcp.yaml

Replace the servers entry with a local command/args, path, or another remote url to use your own MCP server.

Note: When the model emits tool_use, the provider executes the requested MCP tool and returns the raw tool result as the eval output. There is no follow-up Converse turn that feeds the tool result back to the model for a synthesized answer, so the assertions in this example match substrings present in the MCP server's response. If you need a model-summarized answer, wrap the provider in an agent harness or run a second eval over the captured tool output.

Knowledge Base Example

The Knowledge Base example (promptfooconfig.kb.yaml) demonstrates how to use AWS Bedrock Knowledge Base for Retrieval Augmented Generation (RAG).

Knowledge Base Setup

For this example, you'll need to:

1. Create a Knowledge Base in AWS Bedrock
2. Configure it to crawl or ingest content (the example assumes promptfoo documentation content)
3. Use the Amazon Titan Embeddings model for vector embeddings
4. Update the config with your Knowledge Base ID:

providers:
  - id: bedrock:kb:us.anthropic.claude-sonnet-4-6
    config:
      region: 'us-east-2' # Change to your region
      knowledgeBaseId: 'YOUR_KNOWLEDGE_BASE_ID' # Replace with your KB ID

When running the Knowledge Base example, you'll see:

• Responses from a Knowledge Base-enhanced model with citations
• Responses from a standard model for comparison
• Citations from source documents that show where information was retrieved from
• Example of contextTransform feature extracting context from citations for evaluation

The example includes questions about promptfoo configuration, providers, and evaluation techniques that work well with the embedded promptfoo documentation.

Note: You'll need to update the knowledgeBaseId with your actual Knowledge Base ID and ensure the Knowledge Base is configured to work with the selected Claude model.

For detailed Knowledge Base setup instructions, see the AWS Bedrock Knowledge Base Documentation.

Application Inference Profiles Example

The Application Inference Profiles example (promptfooconfig.inference-profiles.yaml) demonstrates how to use AWS Bedrock's inference profiles for multi-region failover and cost optimization.

Key Benefits of Inference Profiles

• Automatic Failover: If one region is unavailable, requests automatically route to another region
• Cost Optimization: Routes to the most cost-effective available model
• Simplified Management: Use a single ARN instead of managing multiple model IDs
• Cross-Region Availability: Access models across multiple regions with a single profile

Configuration Requirements

When using inference profiles, you must specify the inferenceModelType parameter:

providers:
  - id: bedrock:arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/my-profile
    config:
      inferenceModelType: 'claude' # Required!
      region: 'us-east-1'
      max_tokens: 1024

Supported Model Types

• claude - Anthropic Claude models
• nova - Amazon Nova models
• llama - Defaults to Llama 4
• llama2, llama3, llama3.1, llama3.2, llama3.3, llama4 - Specific Llama versions
• mistral - Mistral models
• cohere - Cohere models
• ai21 - AI21 models
• titan - Amazon Titan models
• deepseek - DeepSeek models (with thinking capability)
• openai - OpenAI GPT-OSS models
• zai - Z.AI GLM models
• minimax - MiniMax models
• moonshot - Moonshot Kimi models
• nvidia - NVIDIA Nemotron models
• writer - Writer Palmyra models
• gemma - Google Gemma models

Running the Examples

We provide two inference profile examples:

1. Comprehensive Example (promptfooconfig.inference-profiles.yaml):

   promptfoo eval -c examples/amazon-bedrock/models/promptfooconfig.inference-profiles.yaml
   

   This includes:

   • Multiple inference profiles for different model families
   • Comparison with direct model IDs
   • Use of inference profiles for grading assertions
   • Various model-specific configurations

2. Simple Production Example (promptfooconfig.inference-profiles-simple.yaml):

   promptfoo eval -c examples/amazon-bedrock/models/promptfooconfig.inference-profiles-simple.yaml
   

   This demonstrates:

   • A realistic customer support use case
   • High availability setup with failover
   • Comparison between inference profile and direct model access
   • Consistent grading using inference profiles

Note: Replace the example ARNs with your actual application inference profile ARNs. To create an inference profile, visit the AWS Bedrock console and navigate to the "Application inference profiles" section.

OpenAI Models Example

The OpenAI example (promptfooconfig.openai.yaml) demonstrates OpenAI's GPT-OSS models available through AWS Bedrock:

• openai.gpt-oss-120b-1:0 - 120 billion parameter model with strong reasoning capabilities
• openai.gpt-oss-20b-1:0 - 20 billion parameter model, more cost-effective

Key Features

• Reasoning Effort: Control reasoning depth with low, medium, or high settings
• OpenAI API Format: Uses familiar OpenAI parameters like max_completion_tokens
• Available in us-west-2: Ensure you have model access in the correct region

Run the OpenAI example with:

promptfoo eval -c examples/amazon-bedrock/models/promptfooconfig.openai.yaml

OpenAI Frontier Models Example

The frontier example (promptfooconfig.openai-frontier.yaml) demonstrates OpenAI's GPT-5.x frontier models on Bedrock:

• openai.gpt-5.5 - Flagship frontier reasoning model (available in us-east-2)
• openai.gpt-5.4 - Frontier reasoning model (available in us-east-2 and us-west-2)

Key Features

• Responses API: Frontier models are served through Bedrock's OpenAI-compatible Responses API (the mantle endpoint), not InvokeModel. promptfoo routes bedrock:openai.gpt-5.x there automatically, so output matches the openai:responses provider.

• Bedrock API key auth: Unlike the gpt-oss models (AWS SDK credentials), the frontier models authenticate with an Amazon Bedrock API key. Export it first:

  export AWS_BEARER_TOKEN_BEDROCK="your_bedrock_api_key"
  

• Native Reasoning Effort: reasoning_effort supports none, low, medium, high, and xhigh (minimal is not supported by these Bedrock models).

• Region-gated: Request model access in a supported region before running.

These are the same model IDs that back OpenAI's Codex coding agent when it is configured with the amazon-bedrock provider.

Run the frontier example with:

promptfoo eval -c examples/amazon-bedrock/models/promptfooconfig.openai-frontier.yaml

New Converse API Features (SDK 3.943+)

The Converse API supports additional stop reason handling:

• malformed_model_output: Model produced invalid output
• malformed_tool_use: Model produced a malformed tool use request

These are returned as errors in the response with metadata.isModelError: true.

Nova Sonic Configuration

Nova Sonic now supports configurable timeouts:

providers:
  - id: bedrock:nova-sonic:amazon.nova-sonic-v1:0
    config:
      region: us-east-1
      sessionTimeout: 300000 # 5 minutes (default)
      requestTimeout: 120000 # 2 minutes

Error responses include categorized error types in metadata.errorType:

• connection: Network/AWS connectivity issues
• timeout: Request or session timeout
• api: Authentication/authorization errors
• parsing: Response parsing failures
• session: Bidirectional stream session errors

Getting Started

1. Run the evaluation:

   promptfoo eval -c [path/to/config.yaml]
   

2. View the results:

   promptfoo view