OpenAI Agents

Test multi-turn agentic workflows built with the @openai/agents SDK. Evaluate agents that use tools, hand off between specialists, and handle multi-step tasks.

Prerequisites

• Install SDK: npm install @openai/agents
• Set OPENAI_API_KEY environment variable
• Agent definition (inline or in a TypeScript/JavaScript file)

Basic Usage

providers:
  - openai:agents:my-agent
    config:
      agent:
        name: Customer Support Agent
        model: gpt-5-mini
        instructions: You are a helpful customer support agent.
      maxTurns: 10

Configuration Options

Parameter	Description	Default
agent	Agent definition (inline object or file://path)	-
tools	Additional tool definitions (inline array or file://path)	-
handoffs	Additional handoff definitions (inline array or file://path)	-
maxTurns	Maximum conversation turns	10
model	Override model specified in agent definition	-
modelSettings	SDK ModelSettings overrides, including reasoning, verbosity, and retry settings	-
inputGuardrails	Additional input guardrails (inline array or file://)	-
outputGuardrails	Additional output guardrails (inline array or file://)	-
executeTools	Execute function tools normally (real) or replace them with mocked results	real
toolMocks	Mocked tool outputs keyed by tool name, used when executeTools is mock or false	-
tracing	Enable OpenTelemetry OTLP tracing	false
otlpEndpoint	Custom OTLP endpoint URL for tracing	http://localhost:4318

File-Based Configuration

Load agent and tools from external files:

providers:
  - openai:agents:support-agent
    config:
      agent: file://./agents/support-agent.ts
      tools: file://./tools/support-tools.ts
      maxTurns: 15
      tracing: true

Top-level tools, handoffs, inputGuardrails, and outputGuardrails augment whatever is already defined on the loaded agent.

Example agent file (agents/support-agent.ts):

import { Agent } from '@openai/agents';

export default new Agent({
  name: 'Support Agent',
  model: 'gpt-5-mini',
  instructions: 'You are a helpful customer support agent.',
});

Example tools file (tools/support-tools.ts):

import { tool } from '@openai/agents';
import { z } from 'zod';

export const lookupOrder = tool({
  name: 'lookup_order',
  description: 'Look up order status by order ID',
  parameters: z.object({
    order_id: z.string().describe('The order ID'),
  }),
  execute: async ({ order_id }) => {
    return { status: 'shipped', tracking: 'ABC123' };
  },
});

export default [lookupOrder];

Agent Handoffs

Transfer conversations between specialized agents:

providers:
  - openai:agents:triage
    config:
      agent:
        name: Triage Agent
        model: gpt-5-mini
        instructions: Route questions to the appropriate specialist.
      handoffs:
        - agent:
            name: Technical Support
            model: gpt-5-mini
            instructions: Handle technical troubleshooting.
          description: Transfer for technical issues

Guardrails

Validate tool inputs and outputs with guardrails:

providers:
  - openai:agents:secure-agent
    config:
      agent: file://./agents/secure-agent.ts
      inputGuardrails: file://./guardrails/input-guardrails.ts
      outputGuardrails: file://./guardrails/output-guardrails.ts

Guardrails run validation logic before tool execution (input) and after (output), enabling content filtering, PII detection, or custom business rules.

Retry Policies

OpenAI Agents SDK v0.7 added opt-in retry settings on modelSettings.retry. Promptfoo supports YAML-friendly retry policy presets and passes them to the SDK as runtime callbacks.

providers:
  - openai:agents:support-agent
    config:
      agent: file://./agents/support-agent.ts
      modelSettings:
        retry:
          maxRetries: 2
          backoff:
            initialDelayMs: 250
            maxDelayMs: 2000
            multiplier: 2
            jitter: true
          policy:
            any:
              - providerSuggested
              - httpStatus:
                  - 429
                  - 503

Supported preset policies are never, providerSuggested, networkError, and retryAfter.

You can also compose them with any or all. If you are configuring Promptfoo in TypeScript or JavaScript instead of YAML, you can pass SDK retry callbacks directly.

Mock Tool Execution

Use mocked tool outputs when you want deterministic evals without calling external systems:

providers:
  - openai:agents:support-agent
    config:
      agent: file://./agents/support-agent.ts
      tools: file://./tools/support-tools.ts
      executeTools: mock
      toolMocks:
        lookup_order:
          status: shipped
          tracking: ABC123

Tracing

Enable OpenTelemetry tracing to debug agent execution:

providers:
  - openai:agents:my-agent
    config:
      agent: file://./agents/my-agent.ts
      tracing: true # Exports to http://localhost:4318

With a custom OTLP endpoint:

providers:
  - openai:agents:my-agent
    config:
      agent: file://./agents/my-agent.ts
      tracing: true
      otlpEndpoint: https://otel-collector.example.com:4318

Or enable globally:

export PROMPTFOO_TRACING_ENABLED=true
npx promptfoo eval

Traces include agent execution spans, tool invocations, model calls, handoff events, and token usage.

Once Promptfoo is collecting those traces, you can assert on the agent's path instead of only its final message:

tests:
  - vars:
      query: 'Find order 123 and tell me whether it shipped'
    assert:
      - type: trajectory:tool-used
        value: search_orders

      - type: trajectory:tool-args-match
        value:
          name: search_orders
          args:
            order_id: '123'

      - type: trajectory:tool-sequence
        value:
          steps:
            - search_orders
            - compose_reply

      - type: trajectory:goal-success
        value: 'Determine whether order 123 shipped and tell the user the correct status'
        provider: openai:gpt-5-mini

See Tracing for the eval-level OTLP setup required when you want Promptfoo to ingest and evaluate these traces directly.

Example: D&D Dungeon Master

Full working example with D&D mechanics, dice rolling, and character management:

description: D&D Adventure with AI Dungeon Master

prompts:
  - '{{query}}'

providers:
  - id: openai:agents:dungeon-master
    config:
      agent: file://./agents/dungeon-master-agent.ts
      tools: file://./tools/game-tools.ts
      maxTurns: 20
      tracing: true

tests:
  - description: Dragon combat with attack roll
    vars:
      query: 'I draw my longsword and attack the red dragon!'
    assert:
      - type: llm-rubric
        value: Response includes dice rolls for attack and damage

  - description: Check character stats
    vars:
      query: 'What are my character stats and current HP?'
    assert:
      - type: contains-any
        value: ['Thorin', 'Fighter', 'level 5']

tip

Try the interactive example: npx promptfoo@latest init --example openai-agents-basic

Environment Variables

Variable	Description
OPENAI_API_KEY	OpenAI API key (required)
PROMPTFOO_TRACING_ENABLED	Enable tracing globally
OPENAI_BASE_URL	Custom OpenAI API base URL
OPENAI_ORGANIZATION	OpenAI organization ID

Limitations

warning

Tools must be async functions. Synchronous tools will cause runtime errors.

• Agent definition files must be TypeScript or JavaScript
• File paths require file:// prefix (relative paths resolve from config file location)
• Default maximum: 10 turns (configure with maxTurns)

Related Documentation

• OpenAI Provider - Standard OpenAI completions and chat
• Red Team Guide - Test agent safety
• Assertions - Validate agent responses
• OpenAI Agents SDK - Official SDK documentation