Floop FAQs

Question 1

What is 'spreading activation' and how does it benefit my AI agents?

Accepted Answer

Spreading activation is Floop's graph-based memory retrieval system, inspired by cognitive science. It doesn't just find direct matches; it propagates 'energy' to related behaviors, pulling in associative context to enrich the AI's understanding and provide more relevant guidance.

Question 2

What is Floop and what problem does it solve?

Accepted Answer

Floop provides persistent memory for AI coding agents, empowering them to learn from your corrections, transform them into durable behaviors, and maintain consistent performance across development sessions. It solves the problem of AI agents forgetting past feedback or making repetitive mistakes.

Question 3

Can Floop integrate with my existing AI development tools?

Accepted Answer

Yes, Floop functions as an MCP (Model Context Protocol) server. This allows seamless integration with any AI tool that supports the Model Context Protocol, including popular platforms like Claude Code, Cursor, and other compatible clients, enabling automatic behavior loading.

Question 4

How does Floop learn and apply behaviors?

Accepted Answer

When you correct an AI agent, Floop captures that feedback and extracts reusable 'behaviors.' These behaviors are stored in an associative graph and activated context-awarely based on factors like file type, task, and semantic relevance, using a spreading activation mechanism.

Question 5

Does Floop help optimize token usage for AI models?

Accepted Answer

Absolutely. Floop is token-optimized, meaning it intelligently assembles and injects context – including learned behaviors and associative memories – within your AI model's budget limits, ensuring efficient and cost-effective operation without sacrificing context quality.

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Key Features

Use Cases

Key Features

Use Cases