Cascadeflow
Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop.
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What is Cascadeflow?
cascadeflow is an agent runtime intelligence layer that sits inside AI agent execution and continuously optimizes outcomes across business and technical constraints in real time. It is not another model router but a decision system inside the agent loop where every model call, tool call, and sub-agent handoff can be measured, scored, and steered.
cascadeflow optimizes across six dimensions simultaneously: cost (USD per LLM call), latency (wall-clock milliseconds), quality (model quality priors), budget (cumulative spend tracking and caps), compliance (model allowlists per regulation), and energy (compute-intensity coefficients for carbon-aware model selection). It uses speculative execution paired with quality validation, trying small fast models first and only escalating when genuinely needed.
The tool offers Observe Mode for zero-change visibility where you can add cascadeflow to existing projects with zero code changes and track cost, latency, and model usage across all LLM calls. It works with every major framework including LangChain, LangGraph, OpenAI Agents SDK, CrewAI, Google ADK, n8n, and Vercel AI SDK for both Python and TypeScript.
cascadeflow is designed for AI developers, engineering teams building AI agents, and enterprises running AI workloads who need cost efficiency without sacrificing quality. It reduces AI inference costs by 60-90% while preserving quality and speed, enabling both enterprise and independent developers to initiate and oversee AI initiatives within budget constraints.
The tool is entirely open source under the MIT license, featuring type safety, asynchronous architecture, and integrated monitoring capabilities. It supports a variety of providers including OpenAI, Anthropic, Groq, Hugging Face, Together AI, Ollama, and vLLM, operating smoothly across multiple platforms without vendor lock-in.
Cascadeflow pricing
Pricing model: Freemium
cascadeflow is entirely open source under the MIT license and free to use. The core package is installed via pip install cascadeflow for Python or npm install @cascadeflow/core for TypeScript. Provider-specific extras are available (OpenAI, Anthropic, Groq, Hugging Face, Together AI) and framework integrations (LangChain, OpenAI Agents SDK, CrewAI, Google ADK) are included in the open source package. You only pay for the LLM API calls to your chosen providers (OpenAI, Anthropic, Groq, etc.) - cascadeflow itself has no subscription fees or paid tiers.
Cascadeflow pros
- Reduces AI inference costs by 60-90%
- Zero code changes required in observe mode
- Less than 1ms in-process latency overhead
- Works with every major AI framework
- Open source under MIT license
- Supports multiple LLM providers without vendor lock-in
- Six-dimensional optimization (cost, latency, quality, budget, compliance, energy)
- Type-safe with Python and TypeScript support
- Asynchronous architecture
- Integrated monitoring and decision traces
- Speculative execution with quality validation
- Budget caps and per-user daily limits
- GDPR compliance with model allowlists
- Carbon-aware model selection
- 42+ Python and 33+ TypeScript examples on GitHub
- Audit compliance violations before enforcement
- Tracks energy usage per agent run
- Works with local inference via Ollama and vLLM
Cascadeflow cons
- Requires API keys for providers to be set up
- Python framework integrations require Python 3.10+
- vLLM local inference requires Python 3.10-3.13
- Ollama must be installed separately from ollama.ai
- Core dependencies include pydantic, httpx, tiktoken, rich
- Enforcement mode requires moving from observe mode after testing
- Best suited for multi-step agent loops rather than single calls
- Learning curve for harness configuration
Frequently asked questions about Cascadeflow
What is cascadeflow?
cascadeflow is infrastructure that sits inside AI agent execution and continuously optimizes outcomes across business and technical constraints in real time. It is a decision system inside the agent loop, not another model router. Every model call, tool call, and sub-agent handoff can be measured, scored, and steered where cost, delay, and failure actually happen.
How much does cascadeflow cost?
cascadeflow is entirely open source under the MIT license and completely free to use. You only pay for the LLM API calls to your chosen providers like OpenAI, Anthropic, or Groq. There are no subscription fees or paid tiers for cascadeflow itself.
What is Observe Mode?
Observe Mode tracks every LLM call without blocking or modifying any behavior. It is the safest way to start with zero enforcement, no model switching, just metrics. You add cascadeflow.init(mode='observe') before any LLM calls and everything is tracked without changes to your existing code.
Which frameworks does cascadeflow support?
cascadeflow works with every major framework including LangChain/LangGraph, OpenAI Agents SDK, CrewAI, Google ADK, n8n, and Vercel AI SDK. It supports both Python and TypeScript with framework-specific integrations like cascadeflow[langchain] for Python and @cascadeflow/langchain for TypeScript.
How does cascadeflow reduce costs?
cascadeflow uses speculative execution paired with quality validation, trying small fast models first and only escalating to larger models when genuinely needed. This approach reduces production AI costs by 30-90% without sacrificing quality on complex tasks by routing queries intelligently between small and large models.
What providers does cascadeflow support?
cascadeflow supports OpenAI, Anthropic, Groq, Hugging Face, Together AI, Ollama, and vLLM. It auto-detects available providers based on which API keys are set as environment variables like OPENAI_API_KEY, ANTHROPIC_API_KEY, and GROQ_API_KEY.
What is the latency overhead of cascadeflow?
cascadeflow has less than 1ms in-process latency overhead compared to external proxy solutions that add 40-60ms per call. In a 10-step agent loop, external proxies add 400-600ms while cascadeflow adds approximately 0ms since it runs inside the agent loop.
How do I install cascadeflow?
For Python, run pip install cascadeflow. For TypeScript, run npm install @cascadeflow/core. You can also install provider extras like pip install 'cascadeflow[providers]' for OpenAI + Anthropic + Groq, or framework integrations like pip install 'cascadeflow[langchain]' for LangChain.
What six dimensions does cascadeflow optimize?
cascadeflow scores every agent step across six dimensions: Cost (USD per LLM call from pricing table), Latency (wall-clock milliseconds per call), Quality (model quality priors for routing), Budget (cumulative spend tracking and caps), Compliance (model allowlists per regulation like GDPR), and Energy (compute-intensity coefficients for carbon-aware selection).
Can I use cascadeflow with local models?
Yes, cascadeflow supports local inference through Ollama (communicates via HTTP at localhost:11434, no separate Python package needed) and vLLM (install via pip install 'cascadeflow[vllm]' for Python 3.10-3.13). Ollama must be installed separately from ollama.ai.