---
title: cascadeflow
sidebarTitle: cascadeflow
description: The agent runtime intelligence layer. Control cost, latency, quality, compliance, and energy inside every agent step.
---

# The Agent Runtime Intelligence Layer

cascadeflow is infrastructure that sits **inside** AI agent execution and continuously optimizes outcomes across business and technical constraints in real time.

This is not another model router. It is a **decision system inside the agent loop**.

Every model call, tool call, and sub-agent handoff can be measured, scored, and steered — where cost, delay, and failure actually happen.

<CardGroup cols={2}>
  <Card title="Get Started" icon="rocket" href="/get-started/installation">
    Install, observe, enforce, and ship to production in minutes.
  </Card>
  <Card title="Why cascadeflow" icon="lightbulb" href="/why-cascadeflow">
    The business case for inside-the-loop agent intelligence.
  </Card>
</CardGroup>

## Install

<CodeGroup>

```bash pip
pip install cascadeflow
```

```bash npm
npm install @cascadeflow/core
```

</CodeGroup>

```python
import cascadeflow

cascadeflow.init(mode="observe")
# Every OpenAI and Anthropic SDK call is now tracked — zero code changes.
```

## What Makes This Different

| | External Proxy | cascadeflow |
|---|---|---|
| **Where it runs** | HTTP boundary | Inside the agent loop |
| **What it sees** | Request/response pairs | Step count, budget, tool history, quality, domain, business context |
| **What it optimizes** | Cost | Cost + latency + quality + budget + compliance + energy |
| **What it does** | Observes | `allow`, `switch_model`, `deny_tool`, `stop` |
| **Latency overhead** | 40-60ms per call | &lt;1ms in-process |
| **In 10-step agent loop** | 400-600ms added | ~0ms added |

## Three Lines to Govern Any Agent

<CodeGroup>

```python Observe (zero-change)
import cascadeflow
cascadeflow.init(mode="observe")
# All LLM calls tracked. No blocking, no changes.
```

```python Enforce Budget
import cascadeflow
cascadeflow.init(mode="enforce")

with cascadeflow.run(budget=0.50) as session:
    result = await agent.run("Analyze this dataset")
    print(session.summary())
```

```python Decorated Agent
import cascadeflow
cascadeflow.init(mode="enforce")

@cascadeflow.agent(budget=0.20, compliance="gdpr")
async def my_agent(query: str):
    return await llm.complete(query)
```

</CodeGroup>

## Six Dimensions, One Decision

Every agent step is scored across six dimensions simultaneously:

| Dimension | What it controls | Example |
|---|---|---|
| **Cost** | USD per LLM call from pricing table | Budget cap of $0.50 per run |
| **Latency** | Wall-clock milliseconds per call | Max 2000ms per call |
| **Quality** | Model quality priors for routing | 60% weight on quality KPI |
| **Budget** | Cumulative spend tracking and caps | Per-user daily limits |
| **Compliance** | Model allowlists per regulation | GDPR: only approved models |
| **Energy** | Compute-intensity coefficients | Carbon-aware model selection |

## Works With Every Major Framework

| Framework | Python | TypeScript | Type |
|---|---|---|---|
| LangChain / LangGraph | `cascadeflow[langchain]` | `@cascadeflow/langchain` | Callback handler |
| OpenAI Agents SDK | `cascadeflow[openai-agents]` | — | ModelProvider |
| CrewAI | `cascadeflow[crewai]` | — | llm_hooks |
| Google ADK | `cascadeflow[google-adk]` | — | BasePlugin |
| n8n | — | `@cascadeflow/n8n-nodes-cascadeflow` | Community node |
| Vercel AI SDK | — | `@cascadeflow/vercel-ai` | Middleware |

## Explore

<CardGroup cols={3}>
  <Card title="Agent Harness" icon="shield-halved" href="/get-started/agent-harness">
    Configure budget, compliance, KPI, and energy controls.
  </Card>
  <Card title="Agent Loop" icon="arrows-spin" href="/get-started/agent-loop">
    How cascadeflow operates inside multi-step agent execution.
  </Card>
  <Card title="Examples" icon="code" href="/examples/catalog">
    42+ Python and 33+ TypeScript examples on GitHub.
  </Card>
  <Card title="Integrations" icon="puzzle-piece" href="/integrations/overview">
    LangChain, OpenAI Agents, CrewAI, Google ADK, n8n, Vercel AI.
  </Card>
  <Card title="API Reference" icon="book" href="/api-reference/python/init">
    Full Python and TypeScript API documentation.
  </Card>
  <Card title="For Coding Agents" icon="robot" href="/for-coding-agents">
    Canonical facts, repo map, and implementation entry points.
  </Card>
</CardGroup>
