MarcoFLY FrameWork
The MarcoFLY Framework (MFF) is a structured AI interaction protocol that wraps AI conversations in a prompt-based framework to prevent hallucinations, track epistemic reliability, and keep the model accountable. It introduces six degrees of certainty—Certain, Probable, Maybe, Depends, Unknown, and Cannot—each colour-coded, requiring the model to label every claim with an epistemic status. This replaces uniformly confident output with explicit uncertainty signals. MFF consists of seven modular protection layers (L1–L7) that address hallucination prevention, peer review, anti-degradation over long sessions, and other safeguards. It is provider-agnostic and works on Claude, ChatGPT, Gemini, and other platforms via a bring-your-own-key (BYOK) model, with API keys AES-256-GCM encrypted server-side and sessions deletable by the user. The framework aligns with the NIST AI Risk Management Framework and is available as a free open beta through a progressive web app at app.marcofly.app, supporting 17 AI providers. No installation or configuration is required—users paste a prompt document into any AI chat to activate the protocol.
- Category
- Analytics
- Pricing
- Free
Problem solved
AI tools often generate fluent but unverified responses with no built-in mechanism to signal uncertainty, cite sources, or maintain consistency. MFF solves this by imposing epistemic labels and layered validation, reducing hallucinations, scope drift, and overconfidence while providing traceability and control for users in high-stakes domains.
Use cases
- Fact-checking AI responses in academic or scientific research
- Adding governance layers for enterprise AI usage
- Training students to critically evaluate AI output
- Reducing hallucination risk in legal and medical workflows
- Validating technical decisions in IT and software architecture
- Managing multi-provider AI interactions with BYOK
Audience
- Developers
- Researchers
- Business Users
- Enterprise
Pros
- Provider-agnostic prompt protocol works on any AI platform
- Six epistemic labels make uncertainty explicit
- BYOK design with AES-256-GCM encryption
- Aligned with NIST AI RMF
- Free open beta with no installation
Cons
- Open beta may have limited stability
- Requires user discipline to paste and follow protocol
- No native API or programmatic integration described
- Limited public documentation beyond the homepage
- Epistemic labels rely on model cooperation
Official website: https://marcofly.app/
Canonical LambdaBase page: https://www.lambdabase.com/tools/marcofly-framework