promptfoo--promptfoo/site/docs/providers/echo.md

sidebar_label, description

sidebar_label	description
Echo	Configure Echo Provider for testing and debugging LLM integrations with zero-cost pass-through responses, perfect for validating pre-generated outputs locally

Echo Provider

The Echo Provider is a simple utility provider that returns the input prompt as the output. It's particularly useful for testing, debugging, and validating pre-generated outputs without making any external API calls.

Configuration

To use the Echo Provider, set the provider ID to echo in your configuration file:

providers:
  - echo
  # or
  - id: echo
    label: pass through provider

Response Format

The Echo Provider returns a complete ProviderResponse object with the following fields:

• output: The original input string
• cost: Always 0
• cached: Always false
• tokenUsage: Set to { total: 0, prompt: 0, completion: 0 }
• isRefusal: Always false
• metadata: Any additional metadata provided in the context

Usage

The Echo Provider requires no additional configuration and returns the input after performing any variable substitutions.

Example

providers:
  - echo
  - openai:chat:gpt-5-mini

prompts:
  - 'Summarize this: {{text}}'

tests:
  - vars:
      text: 'The quick brown fox jumps over the lazy dog.'
    assert:
      - type: contains
        value: 'quick brown fox'
      - type: similar
        value: '{{text}}'
        threshold: 0.75

In this example, the Echo Provider returns the exact input after variable substitution, while the OpenAI provider generates a summary.

Use Cases and Working with Pre-generated Outputs

The Echo Provider is useful for:

• Debugging and Testing Prompts: Ensure prompts and variable substitutions work correctly before using complex providers.

• Assertion and Pre-generated Output Evaluation: Test assertion logic on known inputs and validate pre-generated outputs without new API calls.

• Testing Transformations: Test how transformations affect the output without the variability of an LLM response.

• Mocking in Test Environments: Use as a drop-in replacement for other providers in test environments when you don't want to make actual API calls.

Evaluating Logged Production Outputs

A common pattern is evaluating LLM outputs that were already generated in production. This allows you to run assertions against real production data without making new API calls.

Use your logged output directly as the prompt:

prompts:
  - '{{logged_output}}'

providers:
  - echo

tests:
  - vars:
      logged_output: 'Paris is the capital of France.'
    assert:
      - type: llm-rubric
        value: 'Answer is factually correct'
      - type: contains
        value: 'Paris'

The echo provider returns the prompt as-is, so your logged output flows directly to assertions without any API calls.

For JSON-formatted production logs, use a default transform to extract specific fields:

prompts:
  - '{{logged_output}}'

providers:
  - echo

defaultTest:
  options:
    # Extract just the response field from all logged outputs
    transform: 'JSON.parse(output).response'

tests:
  - vars:
      # Production logs often contain JSON strings
      logged_output: '{"response": "Paris is the capital of France.", "confidence": 0.95, "model": "gpt-5"}'
    assert:
      - type: llm-rubric
        value: 'Answer is factually correct'
  - vars:
      logged_output: '{"response": "London is in England.", "confidence": 0.98, "model": "gpt-5"}'
    assert:
      - type: contains
        value: 'London'

This pattern is particularly useful for:

• Post-deployment evaluation of production prompts
• Regression testing against known outputs
• A/B testing assertion strategies on historical data
• Validating system behavior without API costs

For loading large volumes of logged outputs, test cases can be generated dynamically from CSV files, Python scripts, JavaScript functions, or JSON.