Skip to main content
cascadeflow provides a CascadeFlowModelProvider that integrates with the OpenAI Agents SDK as an explicit ModelProvider. This is a strong fit for the runtime-intelligence direction because model selection, tool gating, and budget control stay inside the agent loop where the SDK is already making decisions.

​
Install

pip install "cascadeflow[openai-agents]"

​
Quick Start

import asyncio from agents import Agent, Runner import cascadeflow from cascadeflow.integrations.openai_agents import (  CascadeFlowModelProvider,  OpenAIAgentsIntegrationConfig, )  cascadeflow.init(mode="observe")  # Configure integration config = OpenAIAgentsIntegrationConfig(  model_candidates=["gpt-4o-mini", "gpt-4o"],  enable_tool_gating=True, )  provider = CascadeFlowModelProvider(config=config)  agent = Agent(  name="research_agent",  instructions="You are a helpful research assistant.",  model_provider=provider, )  async def main():  with cascadeflow.run(budget=0.50) as session:  result = await Runner.run(agent, "Explain cascadeflow")  print(result.final_output)  print(session.summary())  asyncio.run(main())

​
Features

  • Model candidates: List of models the provider can select from based on harness scoring
  • Tool gating: Block tool calls when max_tool_calls is reached
  • Scoped runs: Use cascadeflow.run() for per-task budget tracking
  • Decision traces: Full audit trail of model selection and tool gating decisions
  • Fail-open: If the harness encounters an error, execution continues with the default model

​
Why This Integration Matters

  • The model provider sits directly on a core agent decision boundary
  • Budget and tool controls become actionable, not only observable
  • Traces explain why the runtime allowed, switched, or blocked a step

​
Configuration

config = OpenAIAgentsIntegrationConfig(  model_candidates=["gpt-4o-mini", "gpt-4o"], # Models to choose from  enable_tool_gating=True, # Block tools at cap )

​
Session Metrics

After a run, session.summary() includes:
  • cost_total: cumulative USD spent
  • budget_remaining: USD left in the budget
  • step_count: number of LLM calls
  • tool_calls: number of tool executions
  • latency_used_ms: total latency
  • energy_used: total energy units
Example on GitHub: integrations/openai_agents_harness.py