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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.

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Install

pip install "cascadeflow[openai-agents]"

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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())

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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

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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

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Configuration

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

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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