Test OpenAI Code Generation Responses
Promptfoo example set covering OpenAI's Responses API - structured output, function calling, reasoning models, MCP, and more.
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Why it matters
Automate the testing of AI-generated code to ensure quality and correctness. This asset helps verify that code produced by models like OpenAI meets specified requirements and standards.
Outcomes
What it gets done
Generate code snippets using OpenAI.
Write and execute tests against generated code.
Review and debug code based on test results.
Integrate AI code generation into testing pipelines.
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/pfoo-openai-responses | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Openai Responses
This promptfoo example collection covers OpenAI's Responses API across structured output, function calling with real local execution, reasoning-effort comparisons, image input, web search, prompt caching, and remote MCP tool use. Use it as a starting config for evaluating a specific OpenAI Responses API feature; production MCP configs need review of what data is shared with third-party servers.
What it does
This is a collection of promptfoo examples for OpenAI's Responses API, covering basic usage across the GPT-5.6 Sol/Terra/Luna and GPT-4.1 models, external JSON-schema response formats, function calling and local function callbacks, reasoning-budget comparisons across GPT-5.1 through GPT-6 Astra, image input, web search, prompt caching, Codex code-generation models, and remote MCP server integration.
When to use - and when NOT to
Use it as a reference set of starting configs for any Responses API feature you need to evaluate - pick the matching config file rather than building from scratch. It requires an OPENAI_API_KEY, and the MCP examples additionally need access to the target remote MCP server; the bundled example uses the public, unauthenticated DeepWiki server, but production MCP use needs careful review of what data is shared with third-party servers.
Inputs and outputs
Each scenario ships as its own promptfooconfig.*.yaml, run individually with npx promptfoo eval -c <file>: the basic config, .external-format.yaml for loading response_format JSON schema from an external file via file://, .function-call.yaml and .function-callback.yaml where the callback variant executes JavaScript functions locally via functionToolCallbacks and returns real results to assertions instead of just the function call, .reasoning.yaml comparing GPT-5.6 tiers against GPT-6 Astra with explicit reasoning budgets, per-model configs for GPT-5.1 through GPT-5.6 and GPT-6 Astra comparing none/medium/high/xhigh/max reasoning effort and verbosity settings, .image.yaml for vision input, .web-search.yaml, .prompt-cache.yaml combining prompt_cache_key, prompt_cache_options, and cached web-search results, .codex.yaml for code generation, and .mcp.yaml for remote MCP tool use with allowlisting, approval settings, and optional auth headers. The GPT-5.6 example compares its Sol, Terra, and Luna tiers, where the bare gpt-5.6 alias routes to Sol and all three support max reasoning, while Codex ultra is only available on Sol and Terra rather than as a Responses API reasoning value. The GPT-6 Astra example requires an OpenAI account with Astra access and supports low, medium, high, xhigh, and max reasoning, with tool calling only available through the Responses API rather than Chat Completions.
Integrations
OpenAI's Responses API across its GPT-5.x, GPT-6 Astra, and Codex model families; the MCP example integrates the DeepWiki remote MCP server; and promptfoo's eval, assertion, and config-loading framework, including external JSON/YAML file references resolved relative to the config location.
Who it's for
Developers evaluating any specific Responses API capability - structured output, function calling with real execution, reasoning-effort tradeoffs, image input, web search, prompt caching, or MCP tool use - who want a working, model-specific starting config instead of building each test harness from the API docs alone.
Source README
openai-responses (OpenAI Responses API Examples)
This directory contains examples for testing OpenAI's Responses API with promptfoo.
You can run this example with:
npx promptfoo@latest init --example openai-responses
cd openai-responses
Examples
Basic Responses API (promptfooconfig.yaml)
Basic example showing how to use the Responses API with GPT-5.6 Sol, Terra, and Luna, plus a GPT-4.1 comparison model.
External Response Format (promptfooconfig.external-format.yaml)
Example demonstrating how to load response_format configuration from external files. This is useful for:
- Reusing complex JSON schemas across multiple configurations
- Managing large schemas in separate files for better organization
- Version controlling schemas independently
This example compares inline vs. external file approach:
- Inline: JSON schema defined directly in the config
- External: JSON schema loaded from
response_format.jsonusingfile://syntax
Function Calling (promptfooconfig.function-call.yaml)
Example demonstrating function calling capabilities with the Responses API.
Function Callbacks (promptfooconfig.function-callback.yaml)
Example showing how to use function callbacks to execute functions locally instead of just returning the function call. This allows you to:
- Execute custom logic when the model calls a function
- Return the result directly to the test assertions
- Test end-to-end workflows including function execution
Key differences from regular function calling:
- Uses
functionToolCallbacksto define JavaScript functions - Functions are executed locally and results are returned
- Perfect for testing tool-using AI agents
Reasoning Models (promptfooconfig.reasoning.yaml)
Compare GPT-5.6 Sol, Terra, and Luna with GPT-6 Astra using explicit reasoning budgets.
GPT-5.1 (promptfooconfig.gpt-5.1.yaml)
Example demonstrating GPT-5.1's key features including:
nonereasoning mode: No reasoning tokens for fastest responses- Verbosity control: Adjustable output length (
low,medium,high) - Reasoning effort levels: Compare
none,medium, andhighreasoning modes - Coding tasks: Optimized for coding and problem-solving workflows
GPT-5.2 (promptfooconfig.gpt-5.2.yaml)
Example comparing GPT-5.2 with different reasoning effort levels:
- none: No reasoning tokens for fastest responses
- medium: Balanced reasoning for most tasks
- high: Maximum reasoning for complex problem-solving
GPT-5.5 (promptfooconfig.gpt-5.5.yaml)
Example comparing GPT-5.5 standard and pro models with different Responses API reasoning settings.
GPT-5.6 (promptfooconfig.gpt-5.6.yaml)
Example comparing the Sol, Terra, and Luna tiers. The gpt-5.6 alias routes to Sol. All tiers support max reasoning; Codex ultra is available for Sol and Terra rather than as a Responses API reasoning value.
GPT-6 Astra (promptfooconfig.gpt-6-astra.yaml)
Example using Astra with Responses, high reasoning, and structured output. Requires an OpenAI account with Astra access. Astra supports low, medium, high, xhigh, and max reasoning; tool calling requires Responses. See the provider documentation for pricing and hosting availability.
Image Processing (promptfooconfig.image.yaml)
Example demonstrating image input capabilities with vision models.
Web Search (promptfooconfig.web-search.yaml)
Example showing web search capabilities.
Prompt Caching (promptfooconfig.prompt-cache.yaml)
Example combining prompt_cache_key, prompt_cache_options, and includedweb_search_call.results payloads in a Responses request.
Codex Models (promptfooconfig.codex.yaml)
Example using Codex models for code generation tasks.
MCP (Model Context Protocol) (promptfooconfig.mcp.yaml)
Example demonstrating OpenAI's MCP integration with remote MCP servers. This example uses the DeepWiki MCP server to query GitHub repositories.
MCP Features Demonstrated:
- Remote MCP server integration
- Tool filtering with
allowed_tools - Approval settings configuration
- Authentication headers (when needed)
Running the Examples
To run any of these examples:
### Basic Responses API example
npx promptfoo eval -c promptfooconfig.yaml
### External response format example
npx promptfoo eval -c promptfooconfig.external-format.yaml
### MCP example
npx promptfoo eval -c promptfooconfig.mcp.yaml
### Function calling example
npx promptfoo eval -c promptfooconfig.function-call.yaml
### Function callbacks example
npx promptfoo eval -c promptfooconfig.function-callback.yaml
### Reasoning models example
npx promptfoo eval -c promptfooconfig.reasoning.yaml
### GPT-5.1 example
npx promptfoo eval -c promptfooconfig.gpt-5.1.yaml
### GPT-5.2 example
npx promptfoo eval -c promptfooconfig.gpt-5.2.yaml
### GPT-5.5 example
npx promptfoo eval -c promptfooconfig.gpt-5.5.yaml
### GPT-5.6 example
npx promptfoo eval -c promptfooconfig.gpt-5.6.yaml
### GPT-6 Astra example
npx promptfoo eval -c promptfooconfig.gpt-6-astra.yaml --no-cache
### Prompt caching example
npx promptfoo eval -c promptfooconfig.prompt-cache.yaml
Prerequisites
- OpenAI API key set in
OPENAI_API_KEYenvironment variable - For MCP examples: Access to remote MCP servers (some may require authentication)
Notes
- The MCP example uses the public DeepWiki MCP server which doesn't require authentication
- For production use with MCP, carefully review the data being shared with third-party servers
- Some MCP servers may require API keys or authentication tokens in the
headersconfiguration - External file references support both JSON and YAML formats
- External files are resolved relative to the config file location
FAQ
Common questions
Discussion
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