Process and Summarize Data from Multiple Sources
A promptfoo example testing conversational AI agents via OpenAI's Responses API against 31 simulated airline-booking personas.
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Why it matters
Automate the extraction, processing, and summarization of data from various sources to gain actionable insights.
Outcomes
What it gets done
Extract relevant information from diverse data inputs.
Index and prepare data for efficient retrieval.
Summarize complex information into concise reports.
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-integration-tau | 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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Steps
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Overview
Integration Tau
A promptfoo example that tests conversational AI agents against a mocked airline-booking system using OpenAI's Responses API with function calling. It ships 31 distinct customer personas - from budget-focused to accessibility-needs travelers - to exercise realistic multi-turn conversations. Use it to evaluate how a function-calling agent handles varied, realistic customer behavior before wiring it into a real booking system.
What it does
A promptfoo example that tests conversational AI agents using OpenAI's Responses API with function calling, built around a simulated airline booking system. It uses mocked airline functions - no real APIs required - so the agent responds to customer requests, calls functions for operations like searching flights and booking tickets, and gets back mock responses with realistic user profiles, flight options, and confirmations. Run it with:
npx promptfoo@latest init --example integration-tau
cd integration-tau
A sample exchange from the example: a customer books a flight from New York to Seattle, is offered a $325 direct flight versus a $295 one-stop, picks the cheaper option, and receives confirmation code CF8X2M1K.
When to use - and when NOT to
Use it to evaluate how well a conversational agent with function calling handles realistic, multi-turn customer interactions across 31 distinct personas - budget travelers focused on lowest price, business travelers needing flexibility and speed, anxious flyers wanting direct routes and front-row seats, VIP customers expecting premium service, and accessibility-focused travelers with special needs. It's a mocked scenario, not a real airline integration, so it isn't a fit for testing against a live booking API.
Inputs and outputs
Requires an OPENAI_API_KEY set in the environment. Run with promptfoo eval and inspect results with promptfoo view.
Integrations
Personas, mocked functions (e.g. adding seat selection or loyalty-program logic), and the model under test are all swappable - so the same scenario doubles as a way to compare function-calling behavior across different AI providers, not just OpenAI's.
Who it's for
Teams building or evaluating conversational booking/support agents that rely on function calling, and anyone who wants a ready-made multi-persona test bed instead of writing edge-case conversations from scratch.
Source README
integration-tau (Tau Simulated User Example)
You can run this example with:
npx promptfoo@latest init --example integration-tau
cd integration-tau
This example demonstrates testing conversational AI agents using OpenAI's Responses API with function calling. It simulates an airline booking system with 31 different customer personas to test how well agents handle realistic conversations.
How It Works
The example uses mocked airline functions to simulate a booking system without requiring real APIs:
- Agent responds to customer requests using conversational AI
- Functions are called for operations like searching flights and booking tickets
- Mock responses provide realistic data (user profiles, flight options, confirmations)
- 31 personas test different customer behaviors and edge cases
Quick Start
- Set your API key:
export OPENAI_API_KEY=your_api_key_here - Run the evaluation:
promptfoo eval - View results:
promptfoo view
What You'll See
Realistic multi-turn conversations between different customer types and the booking agent:
User: I need a flight from New York to Seattle on May 20th
Agent: I'd be happy to help! May I have your user ID?
User: It's mia_li_3668
Agent: Thank you! I found these options: Direct flight $325, One-stop $295
User: I'll take the cheaper United flight
Agent: Perfect! Your flight is confirmed. Confirmation: CF8X2M1K
Customer Personas Tested
- Budget travelers focused on lowest prices
- Business travelers needing flexibility and speed
- Anxious flyers wanting direct routes and front seats
- VIP customers expecting premium service
- Accessibility-focused travelers with special needs
Customization
- Add personas: Create new customer types with different behaviors
- Extend functions: Add seat selection, loyalty programs, etc.
- Test other models: Compare function calling across AI providers
Learn More
For more information about the Simulated User Provider and other promptfoo features, visit the documentation at promptfoo.dev.
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