# lemony-ai/cascadeflow

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3,926 stars · 881 forks · Python · MIT

## Links

- GitHub: https://github.com/lemony-ai/cascadeflow
- Homepage: https://cascadeflow.ai
- awesome-repositories: https://awesome-repositories.com/repository/lemony-ai-cascadeflow.md

## Topics

`agent` `ai` `anthropic` `api` `budgets` `claude` `cost-optimization` `cost-transparency` `google-adk` `gpt` `huggingface` `llm` `model-cascading` `n8n` `ollama` `openai` `python` `together-ai` `typescript` `vllm`

## Description

Cascadeflow is a runtime for AI agents that cuts language model spend by trying an inexpensive drafter model first, validating the answer, and escalating to a stronger verifier only when quality falls short. It also works as a budget and policy enforcement layer inside the agent loop, applying spending caps, compliance rules, and allow, switch-model, deny-tool, or stop actions at each step, with an audit trail of the decisions taken. Each call reports the model used, its cost, and the savings.

What sets it apart is how it plugs into existing code. It can run as a local gateway that speaks the OpenAI and Anthropic APIs, so existing clients gain cascading by changing only their endpoint. It also offers a LangChain chat model wrapper with chain, streaming, and tool support, plus integrations for other agent and workflow frameworks, including n8n nodes. Models from several hosted and local providers can be mixed in one cascade, and the same interface is available in Python and TypeScript. Routing can take into account the domain and complexity of a task, and weighted business priorities such as quality, cost, latency, and energy. Compliance gating can block models that break rules such as GDPR, HIPAA, or PCI.

Around that core, the project covers response validation (rule-based checks, embedding similarity scoring for relevance, and toxicity screening), per-run and per-user budgets with an observe-only mode for gradual rollout, and tracking of cascade costs and model calls without application code changes. It further includes tooling to discover and rank drafter and verifier pairs, routing for delegated subagents, multi-turn agent loops, and hybrid setups that handle most queries on local models and escalate to cloud providers when needed.

## Tags

### Artificial Intelligence & ML

- [Model Cascades](https://awesome-repositories.com/f/artificial-intelligence-ml/model-cascades.md) — Tries a cheap drafter model first, validates its answer, and escalates to a stronger verifier only when needed to cut cost and latency. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Agent Authorization and Budget Controls](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-authorization-and-budget-controls.md) — Applies spending caps, compliance rules, and business priorities inside the agent loop so every model and tool choice stays within set limits. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Agent Budget Enforcement](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-budget-enforcement.md) — Applies per-run and per-user spending caps with automatic stop actions and an observe-only mode for gradual rollout. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Drop-In Gateway Proxies](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/model-integration-interfaces/ai-integration-apis/openai-compatible-apis/anthropic-compatible-endpoints/openai-compatible-endpoints/drop-in-gateway-proxies.md) — Runs a local gateway speaking the OpenAI and Anthropic APIs so existing clients gain cascading by changing only their endpoint. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Cost-Aware Model Routers](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-model-clients/model-request-routing/cost-aware-model-routers.md) — Lets inexpensive models answer most queries while reserving flagship models for harder ones, cutting spend without losing quality. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Tiered Model Cascades](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-model-clients/model-request-routing/multi-model-pipeline-routings/tiered-model-cascades.md) — Runs a small cheap model first, checks its answer against quality thresholds, and escalates to a larger model only when validation fails. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [LLM Operational Cost Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-operational-cost-optimization.md) — Cuts language model spend by answering with inexpensive models first and escalating to stronger, costlier ones only when answer quality requires it.
- [Cost-Quality Model Routing](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-capability-assessment/model-capability-queries/capability-based-routing/cost-quality-model-routing.md) — Routes each query to the most suitable model by domain and complexity and returns the answer with model used and total cost. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Agent Business Policy Injection](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-business-policy-injection.md) — Embeds KPI weights and policy targets in the agent loop so live business priorities shape model and tool choices. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Agent Decision Records](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-decision-records.md) — Records a traceable decision for every model call, tool call, and handoff so teams can review and tune agent behavior. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Agent Framework Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-framework-integrations.md) — Adds cascading and cost control to existing agent and workflow tools through plugins, callbacks, or a drop-in gateway without rewriting application code.
- [Semantic Complexity Routing](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-model-clients/model-request-routing/semantic-complexity-routing.md) — Detects the domain and complexity of each task and routes it to the most suitable specialist model, per skill and per topic. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Model Access Policies](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-model-configurations/model-access-policies.md) — Blocks models that break rules such as GDPR, HIPAA, or PCI, or fall outside a strict allowlist, before any request runs. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [AI Provider Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-provider-orchestrators.md) — Combines models from several hosted and local providers in one cascade through a single universal interface, including cross-provider tool calling. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Budget-Interrupted Run Recovery](https://awesome-repositories.com/f/artificial-intelligence-ml/budget-interrupted-run-recovery.md) — Wraps an agent run in a session enforcing spending and tool-call limits, reporting cost, latency, energy, steps, and a full audit trail. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Cascading Chat Model Wrappers](https://awesome-repositories.com/f/artificial-intelligence-ml/chat-model-integrations/cascading-chat-model-wrappers.md) — Wraps a drafter and verifier into a single LangChain chat model that supports chains, streaming, and tools in Python and TypeScript. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [LangChain Model Cascading Wrappers](https://awesome-repositories.com/f/artificial-intelligence-ml/langchain-model-cascading-wrappers.md) — Offers a LangChain chat model wrapper pairing a drafter and verifier, with chain, streaming, and tool support in Python and TypeScript.
- [Model Provider Routing](https://awesome-repositories.com/f/artificial-intelligence-ml/model-provider-routing.md) — Sends each request to a suitable model across hosted and local providers, based on task domain, complexity, and quality checks.
- [Model Routing Gateways](https://awesome-repositories.com/f/artificial-intelligence-ml/model-routing-gateways.md) — Runs a local gateway speaking the OpenAI and Anthropic APIs that picks a model per request by domain, complexity, budget, and business priorities.
- [Priority-Weighted Model Selection](https://awesome-repositories.com/f/artificial-intelligence-ml/priority-weighted-model-selection.md) — Feeds quality, cost, latency, and energy weights into every model selection decision to guide the final choice. ([source](https://github.com/lemony-ai/cascadeflow#readme))

### Security & Cryptography

- [Agent Action Policies](https://awesome-repositories.com/f/security-cryptography/identity-access-management/access-control/policy-enforcement-engines/action-resolution-policies/agent-action-policies.md) — Steers each agent step with allow, switch-model, deny-tool, or stop actions based on current context, budget, and policy state. ([source](https://github.com/lemony-ai/cascadeflow#readme))
- [Routing Decision Audit Trails](https://awesome-repositories.com/f/security-cryptography/compliance-audit-tools/authorization-audit-trails/routing-decision-audit-trails.md) — Records a step-by-step trace of action, reason, chosen model, cost, budget state, and enforcement status for review and compliance. ([source](https://github.com/lemony-ai/cascadeflow#readme))

### DevOps & Infrastructure

- [LLM Token Cost Tracking](https://awesome-repositories.com/f/devops-infrastructure/compute-cost-tracking/llm-token-cost-tracking.md) — Records total, drafter, and verifier spend plus token usage for each cascade call, with callbacks compatible with common cost tracking tools. ([source](https://github.com/lemony-ai/cascadeflow#readme))

### System Administration & Monitoring

- [Decision Auditing Hooks](https://awesome-repositories.com/f/system-administration-monitoring/decision-auditing-hooks.md) — Records a traceable step-by-step history of model choices, costs, and enforcement actions so teams can review and tune agent behavior.

### Testing & Quality Assurance

- [Response Quality Gating](https://awesome-repositories.com/f/testing-quality-assurance/code-quality-review/agentic-code-reviews/response-quality-gating.md) — Checks each draft answer with rule-based, similarity, and toxicity validation so weak or unsafe replies are caught before escalation or delivery.
- [Response Quality Scoring](https://awesome-repositories.com/f/testing-quality-assurance/response-quality-scoring.md) — Checks each draft answer with automatic rules and optional similarity scoring to decide whether to accept it or escalate to a stronger model. ([source](https://github.com/lemony-ai/cascadeflow#readme))